CN111341334A - Noise reduction and abnormal sound detection system and method applied to rail transit - Google Patents
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Abstract
The invention discloses a noise reduction and abnormal sound detection system and method applied to rail transit, which comprises a data acquisition module, a data transmission module and a monitoring module, wherein the data acquisition module comprises a noise acquisition module and an audio coding module, the data acquisition module is used for acquiring and coding sound in rail transit vehicles in real time, the data transmission module transmits the acquired sound to the detection module through wireless transceiving equipment, and the monitoring module comprises a vehicle monitoring module and a safety alarm module.
Description
Technical Field
The invention particularly relates to the technical field of rail transit, in particular to a noise reduction and abnormal sound detection system and method applied to rail transit.
Background
With the rapid development of rail transit, rail transit becomes an important vehicle for daily trips of urban residents, and rail transit has many passengers and large passenger flow, which becomes a big problem of safe operation of rail transit at present. The video monitoring system introduced later provides comprehensive on-site monitoring information for the operation department to a certain extent, and the operation department can know the safety condition in the rail station in real time through the information. Because these monitoring systems do not have the audio monitoring function, for some special events, only video monitoring is used, and the monitoring is ineffective, such as sudden surging events, explosion events, shouting, calling for help and the like.
The existing rail transit only records the road condition of a vehicle, can not give out early warning to emergency, and audio monitoring is often difficult to achieve a good sound receiving effect due to the influence of environmental noise.
Disclosure of Invention
The present invention is directed to overcome the above problems in the conventional art, and provides a noise reduction and abnormal sound detection system and method for rail transit.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme: the utility model provides a be applied to making an uproar and unusual sound detecting system fall of track traffic, includes data acquisition module, data transmission module and monitoring module, the data acquisition module includes noise collection module, audio coding module, the data acquisition module is arranged in gathering track traffic vehicle's sound and coding in real time, the data transmission module passes through wireless transceiver and transmits the detection module with the sound of gathering, and the monitoring module includes vehicle monitoring module and safety alarm module.
The noise reduction and abnormal sound detection method applied to rail transit comprises the following steps of S1, collecting sound signals of rail transit vehicles by a noise collection module;
step S2, the detection module is used for processing, identifying and alarming the collected sound;
and step S3, sound energy detection is carried out on the detection module layer, effective rail vehicle sound signals are screened out by detecting the captured rail vehicle sound energy, sound under a quiet condition is eliminated, the execution efficiency of the system is improved, the real-time captured sound signal energy is the sum of short-time energy of all frames, and the effective signals are selected for next processing by comparing the sound signal energy with a set threshold value.
Step S4, in the process of noise reduction of the sound by the detection module, the collected sound of the rail vehicle is subjected to noise reduction processing by improved spectral subtraction method, and the spectral subtraction method is based on the premise that additive noise and sound signals are independent
Further, step S1 transmits the sound signal to the audio acquisition coding processing module in real time to encode the audio signal, and the wireless transceiver of the data transmission module transmits the encoded sound data to the detection module through Wi-Fi.
Further, the processing, identifying and alarming in step S2 includes: the method comprises the steps of sound energy detection, noise reduction processing, blind source separation, sound feature extraction, vehicle sound detection and abnormal sound alarm mechanism.
Further, in step S3, a noise collection module is used to collect sound data, the collected sound data are labeled to determine a sound category to which each piece of sound data belongs, the driving condition of the vehicle is actually represented according to a certain piece of sound data, the sound category to which the piece of sound belongs is labeled as "normal sound" or "abnormal sound", the sound data to which the sound category to which the piece of sound belongs is labeled is used as a training sample, and deep learning is performed on the training sample to obtain an algorithm model, i.e., a sound recognition model, which can accurately distinguish different types of sounds, the abnormal sounds are defined according to different locations according to different requirements of different rail transit, and the obtained model can accurately analyze corresponding abnormal sounds.
Further, in the step S4, if the pure sound signal of the rail vehicle is S (t) and the noise signal is n (t), the noise-containing sound signal y (t) can be represented as: y (w) ═ s (t) + n (t), s (w), n (w), and y (w) are s (t), n (t), and y (t), respectively, fourier transforms of y (w) ═ s (w) + n (w), (w) | y (w) | s (w) | + | n (w) | +2Re [ s (w) n (w) | E (| y (w) | E (| s (w) | + E (| n (w) |) +2E { Re [ s (w) n (w) } n), and s (t) and n (t) are independent of each other, so s (w), n (w) are also independent of each other, and E { Re [ s (w) (w) } n) (w) } 0. Thus, from the above equation: e (| Y (w) |) | E (| S (w)) |) + E (| N (w)) | Y (w)) | S (w)) | + | N (w)) |, some "noise frames" are extracted from the track and used as initial data of a noise library, the noise library is expanded and updated by detecting the energy of collected sound in real time, and when enough noise sections cannot be extracted in the process of performing spectral subtraction, the latest data in the noise library is used as a "silence frame" to estimate noise n (t), so that spectral subtraction noise reduction is completed.
The benefit effects of the invention are: the abnormal sound in the rail transit monitoring range is conveniently detected, the response speed of rail operation personnel to emergencies can be improved to a certain extent, different abnormal sounds are defined according to different places, and the corresponding abnormal sound can be accurately analyzed.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a data collection module according to the present invention;
FIG. 2 is a block diagram of a monitoring module according to the present invention;
FIG. 3 is a schematic diagram of a voice detection circuit of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-3, the present embodiment is a noise reduction and abnormal sound detection system for rail transit, including a data acquisition module, a data transmission module and a monitoring module, where the data acquisition module includes a noise acquisition module and an audio coding module, the data acquisition module is used to acquire and code sound in rail transit vehicles in real time, the data transmission module transmits the acquired sound to the detection module through a wireless transceiver, and the monitoring module includes a vehicle monitoring module and a safety alarm module.
A noise reduction and abnormal sound detection method applied to rail transit comprises the following steps:
s1, collecting the sound signals of the rail transit vehicle by a noise collection module;
step S2, the detection module is used for processing, identifying and alarming the collected sound;
and step S3, sound energy detection is carried out on the detection module layer, effective rail vehicle sound signals are screened out by detecting the captured rail vehicle sound energy, sound under a quiet condition is eliminated, the execution efficiency of the system is improved, the real-time captured sound signal energy is the sum of short-time energy of all frames, and the effective signals are selected for next processing by comparing the sound signal energy with a set threshold value.
Step S4, in the process of noise reduction of the sound by the detection module, the collected sound of the rail vehicle is subjected to noise reduction processing by improved spectral subtraction method, and the spectral subtraction method is based on the premise that additive noise and sound signals are independent
Step S1 is to transmit the sound signal to the audio acquisition and coding processing module in real time to encode the sound signal, and the wireless transceiver of the data transmission module transmits the encoded sound data to the detection module through Wi-Fi.
Wherein, the step S2 of processing, identifying and alarming includes: the method comprises the steps of sound energy detection, noise reduction processing, blind source separation, sound feature extraction, vehicle sound detection and abnormal sound alarm mechanism.
In step S3, a noise collection module is used to collect sound data, the collected sound data are labeled to determine a sound category to which each sound data belongs, a vehicle driving situation is actually represented according to a certain section of sound data, the sound category to which the section of sound belongs is labeled as "normal sound" or "abnormal sound", the sound data to which the sound category belongs is labeled is used as a training sample, and deep learning is performed on the training sample to obtain an algorithm model, i.e., a sound recognition model, which can accurately distinguish various different sounds, the abnormal sounds are different according to different rail transit requirements and different places, and the obtained model can accurately analyze corresponding abnormal sounds.
In step S4, if the pure sound signal of the rail vehicle is S (t), the noise signal is n (t), and the noise-containing sound signal y (t) can be represented as: (w) s (w) + n (t), s (w), n (w), and y (w) are fourier transforms of s (t), n (t), and y (t), respectively, such that y (w) + s (w) + n (w) is derived from the extrapolation: l y (w) | s (w) | + | n (w) | +2Re [ s (w) n (w)) ] E (| y (w) |) E (| s (w) |) + E (| n (w)) | +2E { Re [ s (w) n (w) ] } since s (t) and n (t) are independent of each other, s (w) and n (w) are also independent of each other, E { Re [ s (w) n (w)) ] } 0. Thus, from the above equation: e (| Y (w) |) | E (| S (w)) |) + E (| N (w)) | Y (w)) | S (w)) | + | N (w)) |, some "noise frames" are extracted from the track and used as initial data of a noise library, the noise library is expanded and updated by detecting the energy of collected sound in real time, and when enough noise sections cannot be extracted in the process of performing spectral subtraction, the latest data in the noise library is used as a "silence frame" to estimate noise n (t), so that spectral subtraction noise reduction is completed.
One specific application of this embodiment is: the system comprises a noise acquisition module, a detection module, a sound transmission module, a sound acquisition and coding module, a sound transmission module, a Wi-Fi transmission module and a Wi-Fi transmission module, wherein the noise acquisition module is used for acquiring sound signals of rail transit vehicles, the detection module is used for processing, identifying and alarming acquired sound, sound energy detection is carried out on a detection module layer, effective rail vehicle sound signals are screened out by detecting the acquired sound energy of the rail vehicles, sound under a quiet condition is eliminated, the execution efficiency of the system is improved, the real-time acquired sound signal energy is the sum of short-time energy of all frames, effective signals are selected for further processing by comparing the sound signal energy with a set threshold value, the acquired sound signals are subjected to noise reduction by an improved spectral subtraction method in the sound noise reduction process of the detection module, the spectral subtraction method is based on the premise that additive noise and sound signals are mutually independent, the sound signals are transmitted to the audio acquisition and coding processing module The module, processing, discernment, warning include: sound energy detection, noise reduction processing, blind source separation, sound feature extraction, vehicle sound detection and abnormal sound alarm mechanism, wherein in step S3, a noise acquisition module is adopted to acquire sound data, the acquired sound data are marked to determine the sound category to which each sound data belongs, the vehicle driving condition is actually represented according to a certain section of sound data, the sound category to which the section of sound belongs is marked as 'normal sound' or 'abnormal sound', the sound data marked with the sound category to which the sound data belongs is taken as a training sample, an algorithm model, namely a sound identification model, is obtained by deep learning the training sample, the sound identification model can accurately distinguish different sounds, the abnormal sounds define different abnormal sounds according to different rail traffic requirements and different places, and the obtained model can accurately analyze the corresponding abnormal sounds, if the clean sound signal of the rail vehicle is s (t) and the noise signal is n (t), the noise-containing sound signal y (t) can be represented as: y (w) ═ s (t) + n (t), s (w), n (w), and y (w) are s (t), n (t), and y (t), respectively, fourier transforms of y (w) ═ s (w) + n (w), (w) | y (w) | s (w) | + | n (w) | +2Re [ s (w) n (w) | E (| y (w) | E (| s (w) | + E (| n (w) |) +2E { Re [ s (w) n (w) } n), and s (t) and n (t) are independent of each other, so s (w), n (w) are also independent of each other, and E { Re [ s (w) (w) } n) (w) } 0. Thus, from the above equation: e (| Y (w) |) | E (| S (w)) |) + E (| N (w)) | Y (w)) | S (w)) | + | N (w)) |, some "noise frames" are extracted from the track and used as initial data of a noise library, the noise library is expanded and updated by detecting the energy of collected sound in real time, and when enough noise sections cannot be extracted in the process of performing spectral subtraction, the latest data in the noise library is used as a "silence frame" to estimate noise n (t), so that spectral subtraction noise reduction is completed.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or the like described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. The utility model provides a be applied to track traffic's noise reduction and unusual sound detecting system which characterized in that: the system comprises a data acquisition module, a data transmission module and a monitoring module, wherein the data acquisition module comprises a noise acquisition module and an audio coding module, the data acquisition module is used for acquiring sound in the rail transit vehicle in real time and coding the sound, the data transmission module transmits the acquired sound to the monitoring module through wireless transceiving equipment, and the monitoring module comprises a vehicle monitoring module and a safety alarm module.
2. The noise reduction and abnormal sound detection method applied to rail transit according to claim 1:
step S1, the noise acquisition module acquires sound signals of the rail transit vehicle;
step S2, the detection module is used for processing, identifying and alarming the collected sound;
step S3, sound energy detection is carried out on the detection module layer, effective rail vehicle sound signals are screened out by detecting the captured rail vehicle sound energy, sound under a quiet condition is eliminated, the execution efficiency of the system is improved, the real-time captured sound signal energy is the sum of short-time energy of all frames, and effective signals are selected for next processing by comparing the sound signal energy with a set threshold value;
and step S4, in the process of sound noise reduction of the detection module, carrying out noise reduction processing on the collected rail vehicle sound through improved spectral subtraction, wherein the spectral subtraction is based on the premise that additive noise and sound signals are independent.
3. The noise reduction and abnormal sound detection method applied to rail transit according to claim 2: and S1, the sound signals are transmitted to the audio acquisition coding processing module in real time to be coded, and the wireless transceiving equipment of the data transmission module transmits the coded sound data to the detection module through Wi-Fi.
4. The noise reduction and abnormal sound detection method applied to rail transit according to claim 2: the step S2 of processing, identifying and alarming includes: the method comprises the steps of sound energy detection, noise reduction processing, blind source separation, sound feature extraction, vehicle sound detection and abnormal sound alarm mechanism.
5. The noise reduction and abnormal sound detection method applied to rail transit according to claim 2: in step S3, a noise collection module is used to collect sound data, the collected sound data are labeled to determine a sound category to which each sound data belongs, a vehicle driving situation is actually represented according to a certain section of sound data, the sound category to which the section of sound belongs is labeled as "normal sound" or "abnormal sound", the sound data to which the sound category to which the section of sound belongs is labeled is used as a training sample, and deep learning is performed on the training sample to obtain an algorithm model, i.e., a sound recognition model, which can accurately distinguish various different sounds, the abnormal sounds are defined according to different locations according to different rail transit requirements, and the obtained model can accurately analyze corresponding abnormal sounds.
6. The noise reduction and abnormal sound detection method applied to rail transit according to claim 2: in the step S4, if the pure sound signal of the rail vehicle is S (t), the noise signal is n (t), and the noise-containing sound signal y (t) can be represented as: (w) s (w) + n (t), s (w), n (w), and y (w) are fourier transforms of s (t), n (t), and y (t), respectively, such that y (w) + s (w) + n (w) is derived from the extrapolation: l y (w) | s (w) | + | n (w) | +2Re [ s (w) n (w)) ] E (| y (w) |) E (| s (w) |) + E (| n (w)) | +2E { Re [ s (w) n (w) ] } since s (t) and n (t) are independent of each other, s (w) and n (w) are also independent of each other, E { Re [ s (w) n (w)) ] } 0. Thus, from the above equation: e (| Y (w) |) | E (| S (w)) |) + E (| N (w)) | Y (w)) | S (w)) | + | N (w)) |, some "noise frames" are extracted from the track and used as initial data of a noise library, the noise library is expanded and updated by detecting the energy of collected sound in real time, and when enough noise sections cannot be extracted in the process of performing spectral subtraction, the latest data in the noise library is used as a "silence frame" to estimate noise n (t), so that spectral subtraction noise reduction is completed.
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