WO2020103353A1 - 多波束选取方法及装置 - Google Patents

多波束选取方法及装置

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Publication number
WO2020103353A1
WO2020103353A1 PCT/CN2019/077022 CN2019077022W WO2020103353A1 WO 2020103353 A1 WO2020103353 A1 WO 2020103353A1 CN 2019077022 W CN2019077022 W CN 2019077022W WO 2020103353 A1 WO2020103353 A1 WO 2020103353A1
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Prior art keywords
beam data
data
frequency correlation
correlation coefficient
frequency
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PCT/CN2019/077022
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English (en)
French (fr)
Inventor
李炯亮
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北京小米智能科技有限公司
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Application filed by 北京小米智能科技有限公司 filed Critical 北京小米智能科技有限公司
Priority to KR1020197017626A priority Critical patent/KR102240490B1/ko
Priority to RU2019127676A priority patent/RU2717912C1/ru
Priority to JP2019528751A priority patent/JP6964666B2/ja
Publication of WO2020103353A1 publication Critical patent/WO2020103353A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W56/00—Synchronisation arrangements
    • H04W56/0055—Synchronisation arrangements determining timing error of reception due to propagation delay
    • H04W56/0065—Synchronisation arrangements determining timing error of reception due to propagation delay using measurement of signal travel time
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/491—Details of non-pulse systems
    • G01S7/4911—Transmitters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/483—Details of pulse systems
    • G01S7/484—Transmitters
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2657—Carrier synchronisation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2647—Arrangements specific to the receiver only
    • H04L27/2655—Synchronisation arrangements
    • H04L27/2666—Acquisition of further OFDM parameters, e.g. bandwidth, subcarrier spacing, or guard interval length
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/005—Circuits for transducers, loudspeakers or microphones for combining the signals of two or more microphones
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/223—Execution procedure of a spoken command
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02166—Microphone arrays; Beamforming

Definitions

  • the present disclosure relates to the technical field of digital signal processing, and in particular to a multi-beam selection method and device.
  • the smart home device is generally provided with a microphone array including multiple sound collection modules, through which the sound signal can be collected, and beamforming is performed according to the collected sound signal to obtain multiple beam data corresponding to different directions, so as to facilitate Perform wake-up word detection on multiple beam data, and wake up the smart home device after detecting that the beam data contains set keywords.
  • a microphone array including multiple sound collection modules, through which the sound signal can be collected, and beamforming is performed according to the collected sound signal to obtain multiple beam data corresponding to different directions, so as to facilitate Perform wake-up word detection on multiple beam data, and wake up the smart home device after detecting that the beam data contains set keywords.
  • parallel wake-up word detection can be performed on the multiple beam data respectively.
  • the parallel wake-up word detection requires more processing resources and requires higher computing power for smart home devices, thereby increasing the manufacturing cost of smart home devices and damaging user experience.
  • the embodiments of the present disclosure provide a multi-beam selection method and device.
  • the technical solution is as follows:
  • a multi-beam selection method including:
  • Corresponding beam frequency correlation coefficients in the multiple beam data and beam data satisfying preset correlation coefficient requirements are selected as target beam data.
  • the frequency sampling data is acquired according to the frequency of each beam data in the multiple beam data.
  • Multiple beam frequency correlation coefficients according to the multiple beam frequency correlation coefficients, the sum of the beam frequency correlation coefficients corresponding to each beam data in the multiple beam data is obtained, and the corresponding beam frequency correlation coefficients in the multiple beam data satisfy the preset correlation coefficients
  • the required beam data is selected as the target beam data.
  • the probability of successfully detecting the wake word in the target beam data among the multiple beam data is high, so when acquiring multiple beam data, there is no need to Beam data is used for wake word detection. Only target beam data can be used for wake word detection, thereby reducing the processing resources required for wake word detection and reducing the need for wake word detection without affecting the speed of wake word detection.
  • the computing capability of the computing device is required, thereby reducing the manufacturing cost of the computing device and improving the user experience.
  • acquiring multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data includes:
  • the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients including:
  • the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data is obtained according to the multiple beam frequency correlation coefficients after the normalization processing.
  • selecting the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data as the target beam data includes:
  • the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, or the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, and the corresponding beam frequency correlation coefficient and the smallest beam data in the multiple beam data The beam data is selected as the target beam data.
  • selecting the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data as the target beam data includes:
  • the candidate beam data is selected as the target beam data.
  • selecting the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data as the target beam data includes:
  • the sound source direction is the direction from the sound collection module to the sound source
  • the candidate beam data is selected as the target beam data.
  • a multi-beam selection device including:
  • Beam data acquisition module used to acquire multiple beam data, and frequency sample each beam data in the multiple beam data
  • the beam frequency correlation coefficient acquisition module is used to acquire multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data, and the beam frequency correlation coefficient is used to indicate that one beam data and multiple beams in the multiple beam data The similarity of another beam data in the beam data;
  • the beam frequency correlation coefficient and acquisition module is used to acquire the beam frequency correlation coefficient and corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients;
  • the target beam data selection module is used to select corresponding beam frequency correlation coefficients and beam data satisfying preset correlation coefficient requirements among the multiple beam data as target beam data.
  • the beam frequency correlation coefficient acquisition module includes:
  • the normalization processing sub-module is used to obtain multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data, and perform normalization processing on each beam frequency correlation coefficient;
  • Beam frequency correlation coefficient and acquisition module including:
  • the beam frequency correlation coefficient and acquisition submodule is used to acquire the beam frequency correlation coefficient and the beam frequency correlation coefficient corresponding to each beam data in the multiple beam data according to the normalized multiple beam frequency correlation coefficients.
  • the target beam data selection module includes:
  • the first target beam data selection submodule is used to combine the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, or the corresponding beam frequency correlation coefficient and the largest beam data and the multiple beams in the multiple beam data The corresponding beam frequency correlation coefficient and the smallest beam data in the data are selected as the target beam data.
  • the target beam data selection module includes:
  • the first candidate beam data selection sub-module is used to select corresponding beam frequency correlation coefficients and beam data satisfying preset correlation coefficient requirements among multiple beam data as candidate beam data;
  • Energy value acquisition sub-module for acquiring energy value of candidate beam data
  • the second target beam data selection submodule is used to select the candidate beam data as the target beam data when the energy value of the candidate beam data meets the preset energy value requirements.
  • the target beam data selection module includes:
  • the first candidate beam data selection sub-module is used to select corresponding beam frequency correlation coefficients and beam data satisfying preset correlation coefficient requirements among the multiple beam data as candidate beam data;
  • the candidate beam collection direction determination submodule is used to determine the candidate beam collection direction, and the candidate beam collection direction is the direction that the sound collection module collecting the candidate beam data faces;
  • the winning selection direction determination sub-module is used to determine the sound source direction according to at least two sets of beam data among multiple beam data, and the sound source direction is the direction from the sound collection module to the sound source;
  • the third target beam data selection submodule is used to select the candidate beam data as the target beam data when the angle difference between the candidate beam collection direction and the sound source direction is less than or equal to the preset angle difference.
  • a multi-beam selection apparatus including:
  • Memory for storing processor executable instructions
  • the processor is configured as:
  • Corresponding beam frequency correlation coefficients in the multiple beam data and beam data satisfying preset correlation coefficient requirements are selected as target beam data.
  • a computer-readable storage medium on which computer instructions are stored, and when the instruction is executed by a processor, any of the steps of the method of the first aspect of the embodiments of the present disclosure .
  • Fig. 1 is an application scenario diagram of a multi-beam selection method according to an exemplary embodiment
  • Fig. 2a is a schematic flowchart of a multi-beam selection method according to an exemplary embodiment
  • Fig. 2b is a schematic flowchart of a multi-beam selection method according to an exemplary embodiment
  • Fig. 2c is a schematic flowchart of a multi-beam selection method according to an exemplary embodiment
  • Fig. 2d is a schematic flowchart of a multi-beam selection method according to an exemplary embodiment
  • Fig. 3 is a schematic flowchart of a multi-beam selection method according to an exemplary embodiment
  • Fig. 4a is a schematic structural diagram of a multi-beam selection device according to an exemplary embodiment
  • Fig. 4b is a schematic structural diagram of a multi-beam selection device according to an exemplary embodiment
  • Fig. 4c is a schematic structural diagram of a multi-beam selection device according to an exemplary embodiment
  • Fig. 4d is a schematic structural diagram of a multi-beam selection device according to an exemplary embodiment
  • Fig. 4e is a schematic structural diagram of a multi-beam selection device according to an exemplary embodiment
  • Fig. 5 is a block diagram of an apparatus according to an exemplary embodiment
  • Fig. 6 is a block diagram of a device according to an exemplary embodiment.
  • the smart home device is generally provided with a microphone array including multiple sound collection modules, through which the sound signal can be collected, and beamforming is performed according to the collected sound signal to obtain multiple beam data corresponding to different directions, so as to facilitate Perform wake-up word detection on multiple beam data, and wake up the smart home device after detecting that the beam data contains set keywords.
  • a microphone array including multiple sound collection modules, through which the sound signal can be collected, and beamforming is performed according to the collected sound signal to obtain multiple beam data corresponding to different directions, so as to facilitate Perform wake-up word detection on multiple beam data, and wake up the smart home device after detecting that the beam data contains set keywords.
  • parallel wake-up word detection can be performed on the multiple beam data respectively.
  • the parallel wake-up word detection requires more processing resources and requires higher computing power for smart home devices, thereby increasing the manufacturing cost of smart home devices and damaging user experience.
  • the technical solution provided by the embodiments of the present disclosure by acquiring multiple beam data and frequency sampling each beam data in the multiple beam data, according to each beam data in the multiple beam data Frequency sampling data to obtain multiple beam frequency correlation coefficients, obtain the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients, and the corresponding beam frequency correlation coefficient and in the multiple beam data
  • the beam data that meets the requirements of the preset correlation coefficient is selected as the target beam data.
  • the sound emitted by the sound source includes the wake word
  • the probability of successfully detecting the wake word in the target beam data among the multiple beam data is high, so when acquiring multiple beam data, there is no need to Beam data is used for wake word detection.
  • the computing capability of the computing device is required, thereby reducing the manufacturing cost of the computing device and improving the user experience.
  • the technical solution provided by the embodiments of the present disclosure relates to the electronic device 100 shown in FIG. 1, wherein the electronic device 100 includes a microphone array 101, and the microphone array 101 includes a plurality of sound collection modules 102, and each sound collection module 102 is separately The device 100 is centered in different directions.
  • An embodiment of the present disclosure provides a multi-beam selection method, as shown in FIG. 2a, including the following steps 201 to 204:
  • step 201 multiple beam data is acquired, and each beam data in the multiple beam data is frequency sampled.
  • the multiple beam data may be understood as dividing the space centered on the electronic device into multiple areas, and each area corresponds to one beam data.
  • Frequency sampling each beam data in the multiple beam data can be understood as frequency sampling each beam data in the multiple beam data in the same sampling interval, and the acquired frequency sampling data includes multiple frequency values.
  • step 202 multiple beam frequency correlation coefficients are obtained according to the frequency sampling data of each beam data in the multiple beam data.
  • the beam frequency correlation coefficient is used to indicate the similarity between one beam data in the multiple beam data and another beam data in the multiple beam data.
  • the index numbers of beams corresponding to any two beam data in the multiple beam data are m 1 and m 2 , Is the frequency sampling value of the beam data whose index number is m 1 , Is the frequency sampling value of the beam data with the index number m 2 of the beam, and the beam frequency correlation coefficient between the beam data corresponding to the two beams respectively able to pass Get, where for In the conjugate transpose of, there are N frequency points for frequency sampling in the beam data, for example, N may be a power of 2, for example N may be equal to 256.
  • step 203 the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data is obtained according to the multiple beam frequency correlation coefficients.
  • the sum of the beam frequency correlation coefficient corresponding to any one of the L beam data can be understood as the beam data and the other L-1 beams
  • the beam frequency correlation coefficients of each beam data in the data are added and obtained, that is, the L-1 beam frequency correlation coefficients are added and obtained.
  • step 204 the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data are selected as the target beam data.
  • selecting the beam frequency correlation coefficient corresponding to the multiple beam data and the beam data satisfying the requirements of the preset correlation coefficient as the target beam data can be understood as the corresponding beam frequency correlation coefficient and the largest of the multiple beam data
  • the beam data, or the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, and the corresponding beam frequency correlation coefficient and the smallest beam data in the multiple beam data are selected as the target beam data.
  • the frequency sampling data is acquired according to the frequency of each beam data in the multiple beam data.
  • Multiple beam frequency correlation coefficients according to the multiple beam frequency correlation coefficients, the sum of the beam frequency correlation coefficients corresponding to each beam data in the multiple beam data is obtained, and the corresponding beam frequency correlation coefficients in the multiple beam data satisfy the preset correlation coefficients
  • the required beam data is selected as the target beam data.
  • the probability of successfully detecting the wake word in the target beam data among the multiple beam data is high, so when acquiring multiple beam data, there is no need to Beam data is used for wake word detection. Only target beam data can be used for wake word detection, thereby reducing the processing resources required for wake word detection and reducing the need for wake word detection without affecting the speed of wake word detection.
  • the computing capability of the computing device is required, thereby reducing the manufacturing cost of the computing device and improving the user experience.
  • step 202 in step 202, acquiring multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data may be achieved through step 2021.
  • step 2021 multiple beam frequency correlation coefficients are acquired according to the frequency sampling data of each beam data in the multiple beam data, and each beam frequency correlation coefficient is normalized.
  • acquiring multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data, and normalizing each beam frequency correlation coefficient can be understood as Get beam frequency correlation coefficient
  • the index numbers of the beams corresponding to the two beam data in the multiple beam data are m 1 and m 2 , Is the frequency sampling value of the beam data whose index number is m 1 , Is the frequency sampling value of the beam data whose index number is m 2 , for Is the conjugate transpose, and there are N frequency points for frequency sampling in the beam data.
  • step 203 obtaining the sum of the beam frequency correlation coefficient corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients can be achieved through step 2031:
  • step 2031 the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data is obtained according to the multiple beam frequency correlation coefficients after the normalization processing.
  • step 204 the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data are selected as the target beam data.
  • Steps 2041 to Step 2043 is realized:
  • step 2041 the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data are selected as candidate beam data.
  • step 2042 the energy value of the candidate beam data is obtained.
  • step 2043 when the energy value of the candidate beam data meets the preset energy value requirement, the candidate beam data is selected as the target beam data.
  • the energy value of the candidate beam data meets the preset energy value requirements, which can be understood as when the energy value of the beam data is greater than or equal to the preset energy threshold, the candidate beam data is selected as the target beam data, where the preset energy threshold It may be a noise energy threshold obtained by sampling noise.
  • the candidate beam data is selected as the target beam data, so as to avoid interference of noise on the selection of multiple beams.
  • step 204 the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the plurality of beam data are selected as the target beam data.
  • Step 2047 is realized:
  • step 2044 the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data are selected as candidate beam data.
  • step 2045 the candidate beam collection direction is determined.
  • the candidate beam collection direction is the direction that the sound collection module that collects the candidate beam data faces.
  • step 2046 the sound source direction is determined according to at least two sets of beam data among the plurality of beam data.
  • the sound source direction is the direction from the sound collection module to the sound source.
  • determining the sound source direction according to at least two sets of beam data in the plurality of beam data may be understood as acquiring the sound source propagation delay between the at least two sets of beam data, and determining the sound source direction according to the propagation delay.
  • step 2047 when the angle difference between the candidate beam collection direction and the sound source direction is less than or equal to the preset angle difference, the candidate beam data is selected as the target beam data.
  • the candidate beam collection direction may be understood as the direction pointed by the candidate beam.
  • the angle difference between the candidate beam collection direction and the sound source direction is less than or equal to the preset angle difference, it can be understood that the sound source is located in the direction pointed by the candidate beam.
  • the sound source is determined according to at least two sets of beam data among the multiple beam data Direction, when the angle difference between the candidate beam collection direction and the sound source direction is less than or equal to the preset angle difference, the candidate beam data is selected as the target beam data, which can ensure that the sound source is located in the direction of the target beam.
  • the success rate of waking word detection is the following.
  • Fig. 3 is a schematic flowchart of a multi-beam selection method according to an exemplary embodiment. As shown in Figure 3, it includes the following steps:
  • step 301 multiple beam data are acquired, and each beam data in the multiple beam data is frequency sampled.
  • step 302 multiple beam frequency correlation coefficients are obtained according to the frequency sampling data of each beam data in the multiple beam data, and each beam frequency correlation coefficient is normalized.
  • step 303 the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data is obtained according to the multiple beam frequency correlation coefficients after the normalization processing.
  • step 304 the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data are selected as candidate beam data.
  • step 305 the energy value of the candidate beam data is obtained.
  • step 306 the candidate beam collection direction is determined.
  • the candidate beam collection direction is the direction that the sound collection module that collects the candidate beam data faces.
  • the sound source direction is determined according to at least two sets of beam data among the multiple beam data, and the sound source direction is the direction from the sound collection module to the sound source.
  • step 308 when the angle difference between the candidate beam collection direction and the sound source direction is less than or equal to the preset angle difference, and the energy value of the candidate beam data meets the preset energy value requirement, the candidate beam data is selected as the target Beam data.
  • the frequency sampling data is acquired according to the frequency of each beam data in the multiple beam data.
  • Multiple beam frequency correlation coefficients according to the multiple beam frequency correlation coefficients, the sum of the beam frequency correlation coefficients corresponding to each beam data in the multiple beam data is obtained, and the corresponding beam frequency correlation coefficients in the multiple beam data satisfy the preset correlation coefficients
  • the required beam data is selected as the target beam data.
  • the probability of successfully detecting the wake word in the target beam data among the multiple beam data is high, so when acquiring multiple beam data, there is no need to Beam data is used for wake word detection. Only target beam data can be used for wake word detection, thereby reducing the processing resources required for wake word detection and reducing the need for wake word detection without affecting the speed of wake word detection.
  • the computing capability of the computing device is required, thereby reducing the manufacturing cost of the computing device and improving the user experience.
  • FIG. 4a is a block diagram of a multi-beam selection device 40 according to an exemplary embodiment.
  • the multi-beam selection device 40 may be an electronic device or a part of an electronic device.
  • the multi-beam selection device 40 may be implemented by software, hardware, or both. The combination of the two becomes part or all of the electronic device. As shown in FIG. 4a, the multi-beam selection device 40 includes:
  • the beam data acquisition module 401 is used to acquire multiple beam data and perform frequency sampling on each beam data in the multiple beam data.
  • the beam frequency correlation coefficient acquisition module 402 is used to acquire multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data, and the beam frequency correlation coefficient is used to indicate that one beam data and multiple beams in the multiple beam data The similarity of another beam data in each beam data.
  • the beam frequency correlation coefficient and acquisition module 403 is used to acquire the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients.
  • the target beam data selection module 404 is used to select corresponding beam frequency correlation coefficients and beam data satisfying preset correlation coefficient requirements among the multiple beam data as target beam data.
  • the beam frequency correlation coefficient acquisition module 402 includes:
  • the normalization processing sub-module 4021 is configured to obtain multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data, and perform normalization processing on each beam frequency correlation coefficient.
  • the beam frequency correlation coefficient and acquisition module 403 includes:
  • the beam frequency correlation coefficient and acquisition sub-module 4031 is used to acquire the beam frequency correlation coefficient and the beam frequency correlation coefficient corresponding to each beam data in the multiple beam data according to the normalized multiple beam frequency correlation coefficients.
  • the target beam data selection module 404 includes:
  • the first target beam data selection sub-module 4041 is used to combine the corresponding beam frequency correlation coefficient and the largest beam data among the multiple beam data, or the corresponding beam frequency correlation coefficient and the largest beam data among the plurality of beam data and a plurality of The corresponding beam frequency correlation coefficient and the smallest beam data in the beam data are selected as the target beam data.
  • the target beam data selection module 404 includes:
  • the first candidate beam data selection submodule 4042 is used to select corresponding beam frequency correlation coefficients and beam data satisfying preset correlation coefficient requirements among the multiple beam data as candidate beam data.
  • the energy value obtaining sub-module 4043 is used to obtain the energy value of the candidate beam data.
  • the second target beam data selection sub-module 4044 is used to select the candidate beam data as the target beam data when the energy value of the candidate beam data meets the preset energy value requirements.
  • the target beam data selection module 404 includes:
  • the first candidate beam data selection submodule 4045 is used to select corresponding beam frequency correlation coefficients and beam data satisfying preset correlation coefficient requirements among the multiple beam data as candidate beam data.
  • the candidate beam collection direction determination submodule 4046 is used to determine the candidate beam collection direction, and the candidate beam collection direction is the direction that the sound collection module that collects the candidate beam data faces.
  • the winning selection direction determination sub-module 4047 is used to determine the sound source direction according to at least two sets of beam data among the multiple beam data.
  • the sound source direction is the direction from the sound collection module to the sound source.
  • the third target beam data selection submodule 4048 is used to select candidate beam data as target beam data when the angle difference between the candidate beam collection direction and the sound source direction is less than or equal to the preset angle difference.
  • An embodiment of the present disclosure provides a multi-beam selection device that can acquire multiple beam data and frequency sample each beam data in the multiple beam data according to each of the multiple beam data Frequency sampling data of each beam data to obtain multiple beam frequency correlation coefficients, obtain beam frequency correlation coefficients corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients, and convert the corresponding beam frequencies in the multiple beam data
  • the correlation coefficient and the beam data that meet the requirements of the preset correlation coefficient are selected as the target beam data.
  • the computing capability of the computing device is required, thereby reducing the manufacturing cost of the computing device and improving the user experience.
  • Fig. 5 is a block diagram of a multi-beam selection device 50 according to an exemplary embodiment.
  • the multi-beam selection device 50 may be an electronic device or a part of an electronic device.
  • the multi-beam selection device 50 includes:
  • a memory 502 for storing instructions executable by the processor 501;
  • the processor 501 is configured as:
  • Corresponding beam frequency correlation coefficients in the multiple beam data and beam data satisfying preset correlation coefficient requirements are selected as target beam data.
  • processor 501 may also be configured as:
  • the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients including:
  • the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data is obtained according to the multiple beam frequency correlation coefficients after the normalization processing.
  • processor 501 may also be configured as:
  • the corresponding beam frequency correlation coefficients in the multiple beam data and the beam data satisfying the preset correlation coefficient requirements are selected as the target beam data, including:
  • the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, or the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, and the corresponding beam frequency correlation coefficient and the smallest beam data in the multiple beam data The beam data is selected as the target beam data.
  • processor 501 may also be configured as:
  • the corresponding beam frequency correlation coefficients in the multiple beam data and the beam data satisfying the preset correlation coefficient requirements are selected as the target beam data, including:
  • the candidate beam data is selected as the target beam data.
  • processor 501 may also be configured as:
  • the corresponding beam frequency correlation coefficients in the multiple beam data and the beam data satisfying the preset correlation coefficient requirements are selected as the target beam data, including:
  • the sound source direction is the direction from the sound collection module to the sound source
  • the candidate beam data is selected as the target beam data.
  • An embodiment of the present disclosure provides a multi-beam selection device that can acquire multiple beam data and frequency sample each beam data in the multiple beam data according to each of the multiple beam data Frequency sampling data of each beam data to obtain multiple beam frequency correlation coefficients, obtain beam frequency correlation coefficients corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients, and convert the corresponding beam frequencies in the multiple beam data
  • the correlation coefficient and the beam data that meet the requirements of the preset correlation coefficient are selected as the target beam data.
  • the computing capability of the computing device is required, thereby reducing the manufacturing cost of the computing device and improving the user experience.
  • Fig. 6 is a block diagram of an apparatus 600 for selecting multiple beams according to an exemplary embodiment.
  • the apparatus 600 is suitable for electronic equipment.
  • the device 600 may be a smart speaker, mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, or the like.
  • the device 600 may include one or more of the following components: a processing component 602, a memory 604, a power component 606, a multimedia component 608, an audio component 610, an input / output (I / O) interface 612, a sensor component 614, and a communication component 616 .
  • the processing component 602 generally controls the overall operations of the device 600, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing element 602 may include one or more processors 620 to execute instructions to complete all or part of the steps in the above method.
  • the processing component 602 may include one or more modules to facilitate interaction between the processing component 602 and other components.
  • the processing component 602 may include a multimedia module to facilitate interaction between the multimedia component 608 and the processing component 602.
  • the memory 604 is configured to not store various types of data to support operation at the device 600. Examples of these data include instructions for any application or method operating on the device 600, contact data, phone book data, messages, pictures, videos, and so on.
  • the memory 604 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable and removable Programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read only memory
  • EPROM erasable and removable Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory flash memory
  • flash memory magnetic disk or optical disk.
  • the power supply component 606 provides power to various components of the device 600.
  • the power component 606 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 600.
  • the multimedia component 608 includes a screen between the device 600 and the user that provides an output interface.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or sliding action, but also detect the duration and pressure related to the touch or sliding operation.
  • the multimedia component 608 includes a front camera and / or a rear camera. When the device 600 is in an operation mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 610 is configured to output and / or input audio signals.
  • the audio component 610 includes a microphone (MIC).
  • the microphone When the device 600 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal.
  • the received audio signal may be further stored in the memory 604 or transmitted via the communication component 616.
  • the audio component 610 further includes a speaker for outputting audio signals.
  • the I / O interface 612 provides an interface between the processing component 602 and a peripheral interface module.
  • the peripheral interface module may be a keyboard, a click wheel, or a button. These buttons may include, but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 614 includes one or more sensors for providing the device 600 with status assessments in various aspects.
  • the sensor component 614 can detect the on / off state of the device 600, and the relative positioning of the components, such as the display and the keypad of the device 600, and the sensor component 614 can also detect the position change of the device 600 or a component of the device 600 The presence or absence of user contact with the device 600, the orientation or acceleration / deceleration of the device 600, and the temperature change of the device 600.
  • the sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact.
  • the sensor component 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 614 may further include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • the communication component 616 is configured to facilitate wired or wireless communication between the device 600 and other devices.
  • the device 600 may access a wireless network based on a communication standard, such as a walkie-talkie private network, WiFi, 2G, 3G, 4G, or 5G, or a combination thereof.
  • the communication component 616 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 616 also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the apparatus 600 may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A gate array (FPGA), controller, microcontroller, microprocessor or other electronic components are implemented to perform the above method.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable A gate array
  • controller microcontroller, microprocessor or other electronic components are implemented to perform the above method.
  • a non-transitory computer-readable storage medium including instructions is also provided, for example, a memory 604 including instructions, which can be executed by the processor 620 of the device 600 to complete the above method.
  • the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, or the like.
  • a non-transitory computer-readable storage medium when instructions in the storage medium are executed by a processor of the device 600, enable the device 600 to perform the above multi-beam selection method, the method includes:
  • Corresponding beam frequency correlation coefficients in the multiple beam data and beam data satisfying preset correlation coefficient requirements are selected as target beam data.
  • acquiring multiple beam frequency correlation coefficients according to the frequency sampling data of each beam data in the multiple beam data includes:
  • the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data according to the multiple beam frequency correlation coefficients including:
  • the beam frequency correlation coefficient sum corresponding to each beam data in the multiple beam data is obtained according to the multiple beam frequency correlation coefficients after the normalization processing.
  • selecting the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data as the target beam data includes:
  • the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, or the corresponding beam frequency correlation coefficient and the largest beam data in the multiple beam data, and the corresponding beam frequency correlation coefficient and the smallest beam data in the multiple beam data The beam data is selected as the target beam data.
  • selecting the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient requirements in the multiple beam data as the target beam data includes:
  • the candidate beam data is selected as the target beam data.
  • selecting the corresponding beam frequency correlation coefficient and the beam data satisfying the preset correlation coefficient among the multiple beam data as the target beam data includes:
  • the sound source direction is the direction from the sound collection module to the sound source
  • the candidate beam data is selected as the target beam data.

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Abstract

提供了一种多波束选取方法及装置。该方法包括：获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样（201）；根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数（202）；根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和（203）；将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据（204）。该多波束选取方法可以在不影响唤醒词检测速度的前提下，减少进行唤醒词检测需要的处理资源，降低了对进行唤醒词检测的运算设备的运算能力要求，从而降低了该运算设备的制造成本，改善了用户体验。

Description

多波束选取方法及装置 技术领域
本公开涉及数字信号处理技术领域，尤其涉及多波束选取方法及装置。
背景技术
随着人工智能技术的迅速发展，智能家居设备的应用越来越广泛。智能家居设备中一般设置有包括多个声音采集模块的麦克风阵列，通过该麦克风阵列可以采集声音信号，并根据所采集的声音信号进行波束成型以获取多个分别对应不同方向的波束数据，以便于在多个波束数据进行唤醒词检测，并在检测到波束数据中包含设定的关键词后唤醒智能家居设备。相关技术中，为了加快唤醒智能家居设备的速度，可以分别对该多个波束数据进行并行唤醒词检测。
虽然上述方案能够加快唤醒智能家居设备的速度，但由于进行并行唤醒词检测需要消耗较多的处理资源，对智能家居设备的运算能力要求较高，从而提高了智能家居设备的制造成本，损害了用户体验。
发明内容
为克服相关技术中存在的问题，本公开的实施例提供一种多波束选取方法及装置。技术方案如下：
根据本公开的实施例的第一方面，提供一种多波束选取方法，包括：
获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样；
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，波束频率相关系数用于指示多个波束数据中一个波束数据与多个波束数据中另一个波束数据的相似度；
根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和；
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
本公开的实施例提供的技术方案中，通过获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，将多个波束数据中对应的波束频率相关系数和满足预设相关系数 要求的波束数据选取为目标波束数据。在上述方案中，由于当声源发出的声音包括唤醒词时，在多个波束数据中的目标波束数据中成功检测到唤醒词的几率较高，因此当获取多个波束数据时，无需对全部波束数据进行唤醒词检测，仅对目标波束数据进行唤醒词检测即可，从而在不影响唤醒词检测速度的前提下，减少了进行唤醒词检测需要的处理资源，降低了对进行唤醒词检测的运算设备的运算能力要求，从而降低了该运算设备的制造成本，改善了用户体验。
在一个实施例中，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，包括：
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理；
根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，包括：
根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
在一个实施例中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和最大的波束数据，或多个波束数据中对应的波束频率相关系数和最大的波束数据以及多个波束数据中对应的波束频率相关系数和最小的波束数据选取为目标波束数据。
在一个实施例中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据；
获取候选波束数据的能量值；
当候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据。
在一个实施例中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据；
确定候选波束采集方向，候选波束采集方向为采集候选波束数据的声音采集模块朝向的方向；
根据多个波束数据中至少两组波束数据确定声源方向，声源方向为从声音采集模块指向声源的方向；
当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，将候选波束数据选取为目标波束数据。
根据本公开的实施例的第二方面，提供一种多波束选取装置，包括：
波束数据获取模块，用于获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样；
波束频率相关系数获取模块，用于根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，波束频率相关系数用于指示多个波束数据中一个波束数据与多个波束数据中另一个波束数据的相似度；
波束频率相关系数和获取模块，用于根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和；
目标波束数据选取模块，用于将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
在一个实施例中，波束频率相关系数获取模块，包括：
归一化处理子模块，用于根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理；
波束频率相关系数和获取模块，包括：
波束频率相关系数和获取子模块，用于根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
在一个实施例中，目标波束数据选取模块，包括：
第一目标波束数据选取子模块，用于将多个波束数据中对应的波束频率相关系数和最大的波束数据，或多个波束数据中对应的波束频率相关系数和最大的波束数据以及多个波束数据中对应的波束频率相关系数和最小的波束数据选取为目标波束数据。
在一个实施例中，目标波束数据选取模块，包括：
第一候选波束数据选取子模块，用于将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据；
能量值获取子模块，用于获取候选波束数据的能量值；
第二目标波束数据选取子模块，用于当候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据。
在一个实施例中，目标波束数据选取模块，包括：
第一候选波束数据选取子模块，用于将多个波束数据中对应的波束频率相关系数和满 足预设相关系数要求的波束数据选取为候选波束数据；
候选波束采集方向确定子模块，用于确定候选波束采集方向，候选波束采集方向为采集候选波束数据的声音采集模块朝向的方向；
胜选方向确定子模块，用于根据多个波束数据中至少两组波束数据确定声源方向，声源方向为从声音采集模块指向声源的方向；
第三目标波束数据选取子模块，用于当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，将候选波束数据选取为目标波束数据。
根据本公开的实施例的第三方面，提供一种多波束选取装置，包括：
处理器；
用于存储处理器可执行指令的存储器；
其中，处理器被配置为：
获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样；
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，波束频率相关系数用于指示多个波束数据中一个波束数据与多个波束数据中另一个波束数据的相似度；
根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和；
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
根据本公开的实施例的第四方面，提供一种计算机可读存储介质，其上存储有计算机指令，该指令被处理器执行时本公开的实施例的第一方面中任一项方法的步骤。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
图1是根据一示例性实施例示出的多波束选取方法的应用场景图；
图2a是根据一示例性实施例示出的多波束选取方法的流程示意图；
图2b是根据一示例性实施例示出的多波束选取方法的流程示意图；
图2c是根据一示例性实施例示出的多波束选取方法的流程示意图；
图2d是根据一示例性实施例示出的多波束选取方法的流程示意图；
图3是根据一示例性实施例示出的多波束选取方法的流程示意图；
图4a是根据一示例性实施例示出的多波束选取装置的结构示意图；
图4b是根据一示例性实施例示出的多波束选取装置的结构示意图；
图4c是根据一示例性实施例示出的多波束选取装置的结构示意图；
图4d是根据一示例性实施例示出的多波束选取装置的结构示意图；
图4e是根据一示例性实施例示出的多波束选取装置的结构示意图；
图5是根据一示例性实施例示出的一种装置的框图；
图6是根据一示例性实施例示出的一种装置的框图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本公开的一些方面相一致的装置和方法的例子。
随着科学技术的高速发展和人们生活水平的不断提高，近年来人工智能技术迅速发展，智能家居设备的应用越来越广泛。
智能家居设备中一般设置有包括多个声音采集模块的麦克风阵列，通过该麦克风阵列可以采集声音信号，并根据所采集的声音信号进行波束成型以获取多个分别对应不同方向的波束数据，以便于在多个波束数据进行唤醒词检测，并在检测到波束数据中包含设定的关键词后唤醒智能家居设备。相关技术中，为了加快唤醒智能家居设备的速度，可以分别对该多个波束数据进行并行唤醒词检测。
虽然上述方案能够加快唤醒智能家居设备的速度，但由于进行并行唤醒词检测需要消耗较多的处理资源，对智能家居设备的运算能力要求较高，从而提高了智能家居设备的制造成本，损害了用户体验。
为了解决上述问题，本公开的实施例提供的技术方案中，通过获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。在上述方案中，由于当声源发出的声音包括唤醒词时，在多个波束数据中的目标波束数据中成功检测到唤醒词的几率较高，因此当获取多个波束数据时，无需对全部波束数据进行唤醒词检测，仅对目标波束数据进行唤醒词检测即可，从而在不影响唤醒词检测速度的前提下，减少了进行唤醒词检测 需要的处理资源，降低了对进行唤醒词检测的运算设备的运算能力要求，从而降低了该运算设备的制造成本，改善了用户体验。
本公开的实施例提供的技术方案，涉及如图1所示的电子设备100，其中电子设备100包括麦克风阵列101，麦克风阵列101包括多个声音采集模块102、每个声音采集模块102分别以电子设备100为中心指向不同的方向。
本公开的实施例提供了一种多波束选取方法，如图2a所示，包括如下步骤201至步骤204：
在步骤201中，获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样。
示例性的，多个波束数据可以理解为将以电子设备为中心的空间划分为多个区域，每个区域对应一个波束数据。对多个波束数据中的每个波束数据进行频率采样，可以理解为以相同的采样区间对多个波束数据中的每个波束数据进行频率采样，所获取的频率采样数据包括多个频率值。
在步骤202中，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数。
其中，波束频率相关系数用于指示多个波束数据中一个波束数据与多个波束数据中另一个波束数据的相似度。
示例性的，多个波束数据中的任意两个波束数据对应的波束的索引号为m 1与m 2，
Figure PCTCN2019077022-appb-000001
为波束的索引号为m 1的波束数据的频率采样值，
Figure PCTCN2019077022-appb-000002
为波束的索引号为m 2的波束数据的频率采样值，这两个波束分别对应的波束数据之间的波束频率相关系数
Figure PCTCN2019077022-appb-000003
可以通过
Figure PCTCN2019077022-appb-000004
获取，其中
Figure PCTCN2019077022-appb-000005
为
Figure PCTCN2019077022-appb-000006
的共轭转置，在波束数据中进行频率采样的频点为N个，例如，N可以为2的幂，例如N可以等于256。
在步骤203中，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
示例性的，当多个波束数据为L个波束数据时，该L个波束数据中的任一个波束数据对应的波束频率相关系数和，可以理解为通过将该波束数据与其他L-1个波束数据中每一个波束数据的波束频率相关系数相加获取，即将L-1个波束频率相关系数相加获取。
在步骤204中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
示例性的，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，可以理解为将多个波束数据中对应的波束频率相关系数和最大的波束数据，或多个波束数据中对应的波束频率相关系数和最大的波束数据以及多个波 束数据中对应的波束频率相关系数和最小的波束数据选取为目标波束数据。
本公开的实施例提供的技术方案中，通过获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。在上述方案中，由于当声源发出的声音包括唤醒词时，在多个波束数据中的目标波束数据中成功检测到唤醒词的几率较高，因此当获取多个波束数据时，无需对全部波束数据进行唤醒词检测，仅对目标波束数据进行唤醒词检测即可，从而在不影响唤醒词检测速度的前提下，减少了进行唤醒词检测需要的处理资源，降低了对进行唤醒词检测的运算设备的运算能力要求，从而降低了该运算设备的制造成本，改善了用户体验。
在一个实施例中，如图2b所示，在步骤202中，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，可以通过步骤2021实现：
在步骤2021中，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理。
示例性的，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理，可以理解为通过
Figure PCTCN2019077022-appb-000007
获取波束频率相关系数
Figure PCTCN2019077022-appb-000008
其中多个波束数据中的两个波束数据对应的波束的索引号为m 1与m 2，
Figure PCTCN2019077022-appb-000009
为波束的索引号为m 1的波束数据的频率采样值，
Figure PCTCN2019077022-appb-000010
为波束的索引号为m 2的波束数据的频率采样值，
Figure PCTCN2019077022-appb-000011
为
Figure PCTCN2019077022-appb-000012
的共轭转置，在波束数据中进行频率采样的频点为N个。
在步骤203中，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，可以通过步骤2031实现：
在步骤2031中，根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
通过获取经过进行归一化处理后的多个波束频率相关系数，并根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，可以降低频率采样数据中频率幅度对波束频率相关系数和的影响，提高了进行唤醒词检测的成功率。
在一个实施例中，如图2c所示，在步骤204中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，可以通过步骤2041至步 骤2043实现：
在步骤2041中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据。
在步骤2042中，获取候选波束数据的能量值。
在步骤2043中，当候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据。
示例性的，候选波束数据的能量值满足预设能量值要求，可以理解为当波束数据的能量值大于或等于预设能量阈值时，将候选波束数据选取为目标波束数据，其中预设能量阈值可以为通过对噪声进行采样所获取的噪声能量阈值。
通过将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据，并获取候选波束数据的能量值，当候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据，可以避免噪声对多波束选取所造成的干扰。
在一个实施例中，如图2d所示，在步骤204中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，可以通过步骤2044至步骤2047实现：
在步骤2044中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据。
在步骤2045中，确定候选波束采集方向。
其中，候选波束采集方向为采集候选波束数据的声音采集模块朝向的方向。
在步骤2046中，根据多个波束数据中至少两组波束数据确定声源方向。
其中，声源方向为从声音采集模块指向声源的方向。
示例性的，根据多个波束数据中至少两组波束数据确定声源方向，可以理解为获取至少两组波束数据之间的声源传播时延，并根据传播时延确定声源方向。
在步骤2047中，当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，将候选波束数据选取为目标波束数据。
示例性的，候选波束采集方向可以理解为该候选波束所指向的方向。当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，可以理解为声源位于该候选波束所指向的方向。
通过将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据，并确定候选波束采集方向，根据多个波束数据中至少两组波束数据确定声源方向，当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时， 将候选波束数据选取为目标波束数据，可以在确保声源位于目标波束所指向的方向，提高了进行唤醒词检测的成功率。
下面通过实施例详细介绍实现过程。
图3是根据一示例性实施例示出的一种多波束选取方法的示意性流程图进行说明。如图3所示，包括以下步骤：
在步骤301中，获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样。
在步骤302中，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理。
在步骤303中，根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
在步骤304中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据。
在步骤305中，获取候选波束数据的能量值。
在步骤306中，确定候选波束采集方向。
其中，候选波束采集方向为采集候选束数据的声音采集模块朝向的方向。
在步骤307中，根据多个波束数据中至少两组波束数据确定声源方向，声源方向为从声音采集模块指向声源的方向。
在步骤308中，当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，且候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据。
本公开的实施例提供的技术方案中，通过获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。在上述方案中，由于当声源发出的声音包括唤醒词时，在多个波束数据中的目标波束数据中成功检测到唤醒词的几率较高，因此当获取多个波束数据时，无需对全部波束数据进行唤醒词检测，仅对目标波束数据进行唤醒词检测即可，从而在不影响唤醒词检测速度的前提下，减少了进行唤醒词检测需要的处理资源，降低了对进行唤醒词检测的运算设备的运算能力要求，从而降低了该运算设备的制造成本，改善了用户体验。
下述为本公开装置实施例，可以用于执行本公开方法实施例。
图4a是根据一个示例性实施例示出的一种多波束选取装置40的框图，多波束选取装置40可以为电子设备也可以为电子设备的一部分，多波束选取装置40可以通过软件、硬件或者两者的结合实现成为电子设备的部分或者全部。如图4a所示，该多波束选取装置40包括：
波束数据获取模块401，用于获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样。
波束频率相关系数获取模块402，用于根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，波束频率相关系数用于指示多个波束数据中一个波束数据与多个波束数据中另一个波束数据的相似度。
波束频率相关系数和获取模块403，用于根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
目标波束数据选取模块404，用于将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
在一个实施例中，如图4b所示，波束频率相关系数获取模块402，包括：
归一化处理子模块4021，用于根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理。
波束频率相关系数和获取模块403，包括：
波束频率相关系数和获取子模块4031，用于根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
在一个实施例中，如图4c所示，目标波束数据选取模块404，包括：
第一目标波束数据选取子模块4041，用于将多个波束数据中对应的波束频率相关系数和最大的波束数据，或多个波束数据中对应的波束频率相关系数和最大的波束数据以及多个波束数据中对应的波束频率相关系数和最小的波束数据选取为目标波束数据。
在一个实施例中，如图4d所示，目标波束数据选取模块404，包括：
第一候选波束数据选取子模块4042，用于将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据。
能量值获取子模块4043，用于获取候选波束数据的能量值。
第二目标波束数据选取子模块4044，用于当候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据。
在一个实施例中，如图4e所示，目标波束数据选取模块404，包括：
第一候选波束数据选取子模块4045，用于将多个波束数据中对应的波束频率相关系数 和满足预设相关系数要求的波束数据选取为候选波束数据。
候选波束采集方向确定子模块4046，用于确定候选波束采集方向，候选波束采集方向为采集候选波束数据的声音采集模块朝向的方向。
胜选方向确定子模块4047，用于根据多个波束数据中至少两组波束数据确定声源方向，声源方向为从声音采集模块指向声源的方向。
第三目标波束数据选取子模块4048，用于当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，将候选波束数据选取为目标波束数据。
本公开的实施例提供一种多波束选取装置，该多波束选取装置可以通过获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。在上述方案中，由于当声源发出的声音包括唤醒词时，在多个波束数据中的目标波束数据中成功检测到唤醒词的几率较高，因此当获取多个波束数据时，无需对全部波束数据进行唤醒词检测，仅对目标波束数据进行唤醒词检测即可，从而在不影响唤醒词检测速度的前提下，减少了进行唤醒词检测需要的处理资源，降低了对进行唤醒词检测的运算设备的运算能力要求，从而降低了该运算设备的制造成本，改善了用户体验。
图5是根据一示例性实施例示出的一种多波束选取装置50的框图，该多波束选取装置50可以为电子设备，也可以为电子设备的一部分，多波束选取装置50包括：
处理器501；
用于存储处理器501可执行指令的存储器502；
其中，处理器501被配置为：
获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样；
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，波束频率相关系数用于指示多个波束数据中一个波束数据与多个波束数据中另一个波束数据的相似度；
根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和；
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
在一个实施例中，上述处理器501还可以被配置为：
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，包 括：
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理；
根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，包括：
根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
在一个实施例中，上述处理器501还可以被配置为：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和最大的波束数据，或多个波束数据中对应的波束频率相关系数和最大的波束数据以及多个波束数据中对应的波束频率相关系数和最小的波束数据选取为目标波束数据。
在一个实施例中，上述处理器501还可以被配置为：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据；
获取候选波束数据的能量值；
当候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据。
在一个实施例中，上述处理器501还可以被配置为：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据；
确定候选波束采集方向，候选波束采集方向为采集候选波束数据的声音采集模块朝向的方向；
根据多个波束数据中至少两组波束数据确定声源方向，声源方向为从声音采集模块指向声源的方向；
当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，将候选波束数据选取为目标波束数据。
本公开的实施例提供一种多波束选取装置，该多波束选取装置可以通过获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。在上述方案中，由于当声源发出的声音包括唤醒词时，在多个波束数据中的目标波束数据中成功检测到唤醒词的几率较高，因此当获取多个波束数据时，无需对全部波束数据进行唤醒词检测，仅对目标波束数据进行唤醒词检测即可，从而在不影响唤醒词检测速度的前提下，减少了进行唤醒词检测需要的处理资源，降低了对进行唤醒词检测的运算设备的运算能力要求，从而降低了该运算设备的制造成本，改善了用户体验。
图6是根据一示例性实施例示出的一种用于选取多波束的装置600的框图，该装置600适用于电子设备。例如，装置600可以是智能音箱、移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等。
装置600可以包括以下一个或多个组件：处理组件602，存储器604，电源组件606，多媒体组件608，音频组件610，输入/输出(I/O)的接口612，传感器组件614，以及通信组件616。
处理组件602通常控制装置600的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理元件602可以包括一个或多个处理器620来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件602可以包括一个或多个模块，便于处理组件602和其他组件之间的交互。例如，处理组件602可以包括多媒体模块，以方便多媒体组件608和处理组件602之间的交互。
存储器604被配置未存储各种类型的数据以支持在装置600的操作。这些数据的示例包括用于在装置600上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器604可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件606为装置600的各种组件提供电力。电源组件606可以包括电源管理***，一个或多个电源，及其他与为装置600生成、管理和分配电力相关联的组件。
多媒体组件608包括在装置600和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器 以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件608包括一个前置摄像头和/或后置摄像头。当装置600处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜***或具有焦距和光学变焦能力。
音频组件610被配置为输出和/或输入音频信号。例如，音频组件610包括一个麦克风(MIC)，当装置600处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器604或经由通信组件616发送。在一些实施例中，音频组件610还包括一个扬声器，用于输出音频信号。
I/O接口612为处理组件602和***接口模块之间提供接口，上述***接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件614包括一个或多个传感器，用于为装置600提供各个方面的状态评估。例如，传感器组件614可以检测到装置600的打开/关闭状态，组件的相对定位，例如所述组件为装置600的显示器和小键盘，传感器组件614还可以检测装置600或装置600一个组件的位置改变，用户与装置600接触的存在或不存在，装置600方位或加速/减速和装置600的温度变化。传感器组件614可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件614还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件614还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件616被配置为便于装置600和其他设备之间有线或无线方式的通信。装置600可以接入基于通信标准的无线网络，如对讲机专网、WiFi，2G、3G、4G或5G，或它们的组合。在一个示例性实施例中，通信组件616经由广播信道接收来自外部广播管理***的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件616还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，装置600可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种包括指令的非临时性计算机可读存储介质，例如包括指令的存储器604，上述指令可由装置600的处理器620执行以完成上述方法。例如，所述非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁 带、软盘和光数据存储设备等。
一种非临时性计算机可读存储介质，当所述存储介质中的指令由装置600的处理器执行时，使得装置600能够执行上述多波束选取方法，所述方法包括：
获取多个波束数据，并对多个波束数据中的每个波束数据进行频率采样；
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，波束频率相关系数用于指示多个波束数据中一个波束数据与多个波束数据中另一个波束数据的相似度；
根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和；
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
在一个实施例中，根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，包括：
根据多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个波束频率相关系数进行归一化处理；
根据多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和，包括：
根据进行归一化处理后的多个波束频率相关系数获取多个波束数据中每个波束数据对应的波束频率相关系数和。
在一个实施例中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和最大的波束数据，或多个波束数据中对应的波束频率相关系数和最大的波束数据以及多个波束数据中对应的波束频率相关系数和最小的波束数据选取为目标波束数据。
在一个实施例中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据；
获取候选波束数据的能量值；
当候选波束数据的能量值满足预设能量值要求时，将候选波束数据选取为目标波束数据。
在一个实施例中，将多个波束数据中对应的波束频率相关系数和满足预设相关系数要 求的波束数据选取为目标波束数据，包括：
将多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为候选波束数据；
确定候选波束采集方向，候选波束采集方向为采集候选波束数据的声音采集模块朝向的方向；
根据多个波束数据中至少两组波束数据确定声源方向，声源方向为从声音采集模块指向声源的方向；
当候选波束采集方向与声源方向之间的角度差小于或等于预设角度差时，将候选波束数据选取为目标波束数据。
本领域技术人员在考虑说明书及实践这里公开的公开后，将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由下面的权利要求指出。
应当理解的是，本公开并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。

Claims (12)

  1. 一种多波束选取方法，其特征在于，包括：
    获取多个波束数据，并对所述多个波束数据中的每个波束数据进行频率采样；
    根据所述多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，所述波束频率相关系数用于指示所述多个波束数据中一个波束数据与所述多个波束数据中另一个波束数据的相似度；
    根据所述多个波束频率相关系数获取所述多个波束数据中每个波束数据对应的波束频率相关系数和；
    将所述多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
  2. 根据权利要求1所述的多波束选取方法，其特征在于，所述根据所述多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，包括：
    根据所述多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个所述波束频率相关系数进行归一化处理；
    所述根据所述多个波束频率相关系数获取所述多个波束数据中每个波束数据对应的波束频率相关系数和，包括：
    根据进行归一化处理后的多个波束频率相关系数获取所述多个波束数据中每个波束数据对应的波束频率相关系数和。
  3. 根据权利要求1所述的多波束选取方法，其特征在于，所述将所述多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
    将所述多个波束数据中对应的波束频率相关系数和最大的波束数据，或所述多个波束数据中对应的波束频率相关系数和最大的波束数据以及所述多个波束数据中对应的波束频率相关系数和最小的波束数据选取为所述目标波束数据。
  4. 根据权利要求1所述的多波束选取方法，其特征在于，所述将所述多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
    将所述多个波束数据中对应的波束频率相关系数和满足所述预设相关系数要求的波束 数据选取为候选波束数据；
    获取所述候选波束数据的能量值；
    当所述候选波束数据的能量值满足预设能量值要求时，将所述候选波束数据选取为所述目标波束数据。
  5. 根据权利要求1所述的多波束选取方法，其特征在于，所述将所述多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据，包括：
    将所述多个波束数据中对应的波束频率相关系数和满足所述预设相关系数要求的波束数据选取为候选波束数据；
    确定候选波束采集方向，所述候选波束采集方向为采集所述候选波束数据的声音采集模块朝向的方向；
    根据所述多个波束数据中至少两组波束数据确定声源方向，所述声源方向为从所述声音采集模块指向声源的方向；
    当所述候选波束采集方向与所述声源方向之间的角度差小于或等于预设角度差时，将所述候选波束数据选取为所述目标波束数据。
  6. 一种多波束选取装置，其特征在于，包括：
    波束数据获取模块，用于获取多个波束数据，并对所述多个波束数据中的每个波束数据进行频率采样；
    波束频率相关系数获取模块，用于根据所述多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，所述波束频率相关系数用于指示所述多个波束数据中一个波束数据与所述多个波束数据中另一个波束数据的相似度；
    波束频率相关系数和获取模块，用于根据所述多个波束频率相关系数获取所述多个波束数据中每个波束数据对应的波束频率相关系数和；
    目标波束数据选取模块，用于将所述多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
  7. 根据权利要求1所述的多波束选取装置，其特征在于，所述波束频率相关系数获取模块，包括：
    归一化处理子模块，用于根据所述多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，并对每个所述波束频率相关系数进行归一化处理；
    所述波束频率相关系数和获取模块，包括：
    波束频率相关系数和获取子模块，用于根据进行归一化处理后的多个波束频率相关系数获取所述多个波束数据中每个波束数据对应的波束频率相关系数和。
  8. 根据权利要求1所述的多波束选取装置，其特征在于，所述目标波束数据选取模块，包括：
    第一目标波束数据选取子模块，用于将所述多个波束数据中对应的波束频率相关系数和最大的波束数据，或所述多个波束数据中对应的波束频率相关系数和最大的波束数据以及所述多个波束数据中对应的波束频率相关系数和最小的波束数据选取为所述目标波束数据。
  9. 根据权利要求1所述的多波束选取装置，其特征在于，所述目标波束数据选取模块，包括：
    第一候选波束数据选取子模块，用于将所述多个波束数据中对应的波束频率相关系数和满足所述预设相关系数要求的波束数据选取为候选波束数据；
    能量值获取子模块，用于获取所述候选波束数据的能量值；
    第二目标波束数据选取子模块，用于当所述候选波束数据的能量值满足预设能量值要求时，将所述候选波束数据选取为所述目标波束数据。
  10. 根据权利要求1所述的多波束选取装置，其特征在于，所述目标波束数据选取模块，包括：
    第一候选波束数据选取子模块，用于将所述多个波束数据中对应的波束频率相关系数和满足所述预设相关系数要求的波束数据选取为候选波束数据；
    候选波束采集方向确定子模块，用于确定候选波束采集方向，所述候选波束采集方向为采集所述候选波束数据的声音采集模块朝向的方向；
    胜选方向确定子模块，用于根据所述多个波束数据中至少两组波束数据确定声源方向，所述声源方向为从所述声音采集模块指向声源的方向；
    第三目标波束数据选取子模块，用于当所述候选波束采集方向与所述声源方向之间的角度差小于或等于预设角度差时，将所述候选波束数据选取为所述目标波束数据。
  11. 一种多波束选取装置，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为：
    获取多个波束数据，并对所述多个波束数据中的每个波束数据进行频率采样；
    根据所述多个波束数据中的每个波束数据的频率采样数据获取多个波束频率相关系数，所述波束频率相关系数用于指示所述多个波束数据中一个波束数据与所述多个波束数据中另一个波束数据的相似度；
    根据所述多个波束频率相关系数获取所述多个波束数据中每个波束数据对应的波束频率相关系数和；
    将所述多个波束数据中对应的波束频率相关系数和满足预设相关系数要求的波束数据选取为目标波束数据。
  12. 一种计算机可读存储介质，其上存储有计算机指令，其特征在于，该指令被处理器执行时实现权利要求1-5任一项所述方法的步骤。
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Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109599104B (zh) * 2018-11-20 2022-04-01 北京小米智能科技有限公司 多波束选取方法及装置
CN111833901B (zh) * 2019-04-23 2024-04-05 北京京东尚科信息技术有限公司 音频处理方法、音频处理装置、***及介质
CN110364161A (zh) 2019-08-22 2019-10-22 北京小米智能科技有限公司 响应语音信号的方法、电子设备、介质及***
WO2021214512A1 (en) * 2020-04-20 2021-10-28 Telefonaktiebolaget Lm Ericsson (Publ) Beam management for a radio transceiver device
CN112492452B (zh) * 2020-11-26 2022-08-26 北京字节跳动网络技术有限公司 波束系数存储方法、装置、设备和存储介质
KR102671092B1 (ko) * 2021-11-19 2024-05-31 썬전 샥 컴퍼니 리미티드 개방형 음향장치

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1664610A (zh) * 2004-03-02 2005-09-07 微软公司 使用传声器阵列聚束的***和方法
CN104661150A (zh) * 2013-11-25 2015-05-27 现代自动车株式会社 用于识别语音的装置和方法
CN104810021A (zh) * 2015-05-11 2015-07-29 百度在线网络技术（北京）有限公司 应用于远场识别的前处理方法和装置
CN105590631A (zh) * 2014-11-14 2016-05-18 中兴通讯股份有限公司 信号处理的方法及装置
US9653060B1 (en) * 2016-02-09 2017-05-16 Amazon Technologies, Inc. Hybrid reference signal for acoustic echo cancellation
CN106952653A (zh) * 2017-03-15 2017-07-14 科大讯飞股份有限公司 噪声去除方法、装置和终端设备

Family Cites Families (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2861011B2 (ja) * 1989-01-11 1999-02-24 アイシン精機株式会社 音声入力方法及び装置
US7426464B2 (en) * 2004-07-15 2008-09-16 Bitwave Pte Ltd. Signal processing apparatus and method for reducing noise and interference in speech communication and speech recognition
EP3273442B1 (en) * 2008-03-20 2021-10-20 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Apparatus and method for synthesizing a parameterized representation of an audio signal
JP2011257627A (ja) * 2010-06-10 2011-12-22 Murata Mach Ltd 音声認識装置と認識方法
GB2495129B (en) * 2011-09-30 2017-07-19 Skype Processing signals
US20130329908A1 (en) * 2012-06-08 2013-12-12 Apple Inc. Adjusting audio beamforming settings based on system state
KR20140108788A (ko) * 2013-02-28 2014-09-15 경북대학교 산학협력단 단일 마이크로폰을 이용한 접촉식 인터페이스 장치
US9489965B2 (en) * 2013-03-15 2016-11-08 Sri International Method and apparatus for acoustic signal characterization
US9905231B2 (en) * 2013-04-27 2018-02-27 Intellectual Discovery Co., Ltd. Audio signal processing method
KR102146462B1 (ko) * 2014-03-31 2020-08-20 삼성전자주식회사 음성 인식 시스템 및 방법
KR102346302B1 (ko) * 2015-02-16 2022-01-03 삼성전자 주식회사 전자 장치 및 음성 인식 기능 운용 방법
US9820036B1 (en) * 2015-12-30 2017-11-14 Amazon Technologies, Inc. Speech processing of reflected sound
CN105791611B (zh) * 2016-02-22 2020-07-07 腾讯科技（深圳）有限公司 回声消除方法、装置、终端以及存储介质
CN106023996B (zh) * 2016-06-12 2019-08-27 杭州电子科技大学 基于十字形声阵列宽带波束形成的声识别方法
US9972337B2 (en) * 2016-06-22 2018-05-15 Cisco Technology, Inc. Acoustic echo cancellation with delay uncertainty and delay change
JP6387151B2 (ja) * 2016-09-30 2018-09-05 パナソニック株式会社 雑音抑圧装置、及び、雑音抑圧方法
KR20180103634A (ko) * 2017-03-10 2018-09-19 주식회사 쿨잼컴퍼니 통계적 방법에 의한 음향 분석 및 비교
KR20180103639A (ko) * 2017-03-11 2018-09-19 주식회사 쿨잼컴퍼니 상대적 유사도에 기초한 음악 시퀀스들의 유사도 분석
CN107274911A (zh) * 2017-05-03 2017-10-20 昆明理工大学 一种基于声音特征的相似度分析方法
CN107221320A (zh) * 2017-05-19 2017-09-29 百度在线网络技术（北京）有限公司 训练声学特征提取模型的方法、装置、设备和计算机存储介质
US10051366B1 (en) * 2017-09-28 2018-08-14 Sonos, Inc. Three-dimensional beam forming with a microphone array
CN108122563B (zh) * 2017-12-19 2021-03-30 北京声智科技有限公司 提高语音唤醒率及修正doa的方法
CN108181612B (zh) * 2017-12-22 2019-05-21 达闼科技（北京）有限公司 确定麦克风波束成型角度的方法及相关装置
CN108717495A (zh) * 2018-05-22 2018-10-30 出门问问信息科技有限公司 多波束波束成形的方法、装置及电子设备
US10622004B1 (en) * 2018-08-20 2020-04-14 Amazon Technologies, Inc. Acoustic echo cancellation using loudspeaker position
CN109599104B (zh) * 2018-11-20 2022-04-01 北京小米智能科技有限公司 多波束选取方法及装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1664610A (zh) * 2004-03-02 2005-09-07 微软公司 使用传声器阵列聚束的***和方法
CN104661150A (zh) * 2013-11-25 2015-05-27 现代自动车株式会社 用于识别语音的装置和方法
CN105590631A (zh) * 2014-11-14 2016-05-18 中兴通讯股份有限公司 信号处理的方法及装置
CN104810021A (zh) * 2015-05-11 2015-07-29 百度在线网络技术（北京）有限公司 应用于远场识别的前处理方法和装置
US9653060B1 (en) * 2016-02-09 2017-05-16 Amazon Technologies, Inc. Hybrid reference signal for acoustic echo cancellation
CN106952653A (zh) * 2017-03-15 2017-07-14 科大讯飞股份有限公司 噪声去除方法、装置和终端设备

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