CN114913869A - Bird acoustic diversity index method with low sensitivity to noise influence - Google Patents
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Abstract
The invention discloses a bird acoustic diversity index method with low sensitivity to noise influence, which comprises the steps of firstly obtaining a time-frequency power spectrum of field bird sound monitoring data in an analysis period through short-time Fourier transform, then calculating a floating detection threshold candidate value of each frequency point meeting the requirement of a local signal-to-noise ratio according to narrow-band noise level estimation, taking a fullness relative level threshold as a frequency-variable detection threshold of which the lower limit value is subjected to time-frequency spectrum binarization processing, and finally performing Shannon index calculation by counting the number of time-frequency points of which the power in each sub-frequency band is higher than the detection threshold to obtain an acoustic diversity index based on frequency-variable threshold detection. The invention can effectively solve the principle defect that ADI is very sensitive to noise influence, simultaneously can be completely compatible with the ecological explanation and discovery of the existing ADI research, provides a new tool for realizing stable and reliable rapid evaluation of bird species diversity, and has very important significance on the ecological environment acoustic monitoring.
Description
Technical Field
The invention relates to the field of rapid evaluation of bird species diversity and acoustic signal processing, in particular to a bird acoustic diversity index method with low sensitivity to noise influence.
Background
In the face of global ecological environment changes, biodiversity monitoring is an increasingly urgent task. The activity of bird singing activities is an important index for reflecting the biological diversity and the ecological environment health degree in an ecological area, and the rapid assessment of the species diversity of birds has important significance for biological diversity monitoring and research.
With the popularization of acoustic monitoring technology and the development of sound-scene ecology, acoustic indexes are widely applied to ecological monitoring and environmental evaluation as a method for rapidly evaluating the diversity of bird species. The acoustic index is usually calculated for a bird song signal recorded by a recording device in the field in advance, but the data recorded by the recording device is not a pure bird song signal but a mixed signal containing various noise influences. The existing acoustic index is extremely sensitive to noise influence, and the actually calculated index result is often inconsistent with the expected ecological significance, so that the acoustic index cannot bear a stable and reliable task of quickly evaluating the diversity of bird species. The invention discloses a noise suppression acoustic index acquisition method based on a differential microphone array, which is disclosed in the Chinese patent publication No. CN 109714689B. The invention discloses an acoustic index determining method, an acoustic index determining device and a storage medium in the Chinese patent application No. CN202111072342.0, wherein the method and the device eliminate common environmental noises such as wind sound, cicada sound, stream sound and the like through noise reduction processing, and simultaneously compensate distortion of recorded bird song signals, thereby reducing the acoustic index result change caused by the performance difference of recording equipment.
However, in practical use, even under ideal conditions where a quiet ecological area free of non-bird song noise and signal distortion caused by a recording apparatus can be ignored, inherent background noise necessarily exists in the recorded data. Even if the intrinsic background Noise is weak, the intensity of the actually recorded bird song Signal is artificially uncontrollable, the Signal-to-Noise Ratio (SNR) in the recorded sound data is uncertain, and the conventional acoustic index has a principle defect of not considering the influence of Noise in design, so that the actual numerical value of the acoustic index is more and more deviated from the ideal value corresponding to the pure bird song Signal along with the reduction of the SNR of the bird song. Therefore, in order to realize the robust and reliable rapid evaluation of the diversity of bird species, the design scheme of the existing acoustic index must be modified in principle, and a new acoustic index method with low sensitivity to noise influence is obtained. But the principles and methods for improving the robustness of their impact on noise are generally different for different acoustic indices.
Disclosure of Invention
The invention aims to provide an acoustic diversity index method for birds with low sensitivity to noise influence.
The technical solution for realizing the purpose of the invention is as follows: an acoustic diversity index method for birds with low susceptibility to noise influence comprises the following steps:
step 3, respectively calculating the binarization detection threshold of each frequency point by using the bird sound monitoring data time-frequency power spectrum obtained in the step 1 and the narrow-band noise average power obtained in the step 2;
step 4, respectively carrying out 0-1 judgment processing on the power of the frequency points when the bird sound monitoring data in each frequency point is carried out by utilizing the time-frequency power spectrum of the bird sound monitoring data obtained in the step 1 and the binarization detection threshold obtained in the step 3, and then respectively counting the number of the frequency points when the power in each sub-band is higher than the value 1 of the detection threshold;
and 5, performing Shannon index calculation by using the number of the time-frequency points with the value of 1 in each sub-frequency band obtained in the step 4 to obtain an acoustic diversity index based on frequency variation threshold detection.
In a second aspect, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of the first aspect when executing the program.
In a third aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect.
In a fourth aspect, the invention provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that: 1) the design defect of the existing index is corrected in principle, and a new bird acoustic diversity index method with low sensitivity to noise influence is provided, namely the acoustic diversity index based on frequency change threshold detection; 2) the influence of the change of the bird song SNR on the calculation result of the actual index is obviously inhibited while the ecological explanation and the discovery of the existing acoustic index research are not influenced, and the SNR lower limit for keeping the index stable is greatly reduced; 3) a completely passive acoustic monitoring mode is adopted, so that the activity of wild birds is not influenced; 4) the method is convenient and fast in implementation process and easy to implement, and provides an efficient means for realizing stable and reliable rapid assessment of the variety of the birds.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
FIG. 1 is a flow chart of a bird acoustic diversity index method with low susceptibility to noise effects.
Fig. 2 is a time domain waveform diagram and a corresponding time frequency spectrum diagram of the monitored recording data with the same time frequency distribution of the bird song signal under different SNR conditions.
FIG. 3 is a graph of index values calculated by each of FADI and ADI for monitored audio recording data having the same time-frequency distribution of birdsong signals as a function of birdsong SNR.
Detailed Description
The literature (Villanueva-river L J, Pijanowski B C, Doucette J, et al. A primer of Acoustic analysis for Landscape algorithms [ J ]. Landscape Ecology,2011,26(9): 1233-. The invention only aims at the principle defect of wider application of ADI in the document A primer of Acoustic analysis for landscaping experiments, and provides a bird Acoustic Diversity Index method with low sensitivity to noise influence, namely an Acoustic Diversity Index (FADI) based on Frequency-dependent Acoustic Diversity Index (Frequency-dependent Acoustic Diversity Index). The time-frequency spectrum binarization processing of the FADI adopts narrow-band floating detection thresholds which are respectively set by taking the average noise power of each frequency point as a reference and simultaneously meeting a certain SNR requirement, and simultaneously, the threshold of the fullness relative level (dB Full Scale, dBFS) of the ADI is taken as the lower limit value of the threshold of each frequency point, and other index calculation processes are the same as those of the ADI. The FADI provided by the invention can ensure that index calculation results are not obviously changed even when the SNR is as low as below 0dB under the condition that the bird song signals have the same time-frequency distribution, and can keep consistent with the existing ADI value at high SNR, thereby not influencing the ecological explanation and discovery of the existing ADI research. The invention provides an efficient means for realizing stable and reliable rapid assessment of species diversity of birds, and provides a new idea for popularization and application of an acoustic signal processing technology in ecological environment monitoring and assessment.
The invention discloses an acoustic diversity index method for birds with low sensitivity to noise influence. The method is oriented to an ecological environment Acoustic monitoring task, a time-Frequency power spectrum of field bird sound monitoring data in an analysis time period is obtained through short-time Fourier transform, then a floating detection threshold candidate value of each Frequency point meeting the requirement of a local signal-to-noise ratio is calculated according to narrow-band noise level estimation, a fullness relative level (dB Scale, dBFS) threshold is used as a Frequency-change detection threshold of time-Frequency spectrum binarization processing of a lower limit value, and finally a Shannon Index is calculated by counting the number of time-Frequency points of which the power in each sub-Frequency band is higher than the detection threshold, so that an Acoustic Diversity Index (FADI) based on Frequency-change threshold detection is obtained.
Referring to fig. 1, the method for indexing acoustic diversity of birds with low susceptibility to noise influence of the present invention comprises the following steps:
step 1-1, performing frame processing on the field birdsound monitoring data acquired in the analysis period, wherein the frame shift is one frame length. After adding Hanning window to each frame of data, carrying out discrete Fourier transform processing to obtain S (K, L), wherein K is more than or equal to 1 and less than or equal to K, and L is more than or equal to 1 and less than or equal to L, wherein K and L are frequency point number and frame number respectively, K is the number of frequency points in analysis bandwidth, L is the number of frames in analysis time period, the analysis bandwidth is 0Hz-8kHz, the data sampling rate is 16kHz, the length of the analysis time period is 600S, and the frame length is 10 ms;
step 1-2, calculating a time-frequency power spectrum of the field bird sound monitoring data in an analysis time period by using the short-time Fourier transform S (k, l) obtained in the step 1-1
P s (k,l)＝|S(k,l)| 2 ，1≤k≤K,1≤l≤L
step 2-1, all time frequency points without bird song signals are taken as noise time frequency points, and a noise time frequency point frame number set corresponding to the kth frequency point is recorded as xi k Set xi k The number of the time frequency points of the noise in the middle is M k ；
Step 2-2, according to the noise time frequency point frame number set xi in each frequency point obtained in step 2-1 k Respectively calculate the average power of the narrow-band noise in each frequency point
Step 3, respectively calculating the binarization detection threshold of each frequency point by using the bird sound monitoring data time-frequency power spectrum obtained in the step 1 and the narrow-band noise average power obtained in the step 2:
step 3-1, calculating a power value corresponding to a-50 dBFS detection threshold of ADI by using the time-frequency power spectrum of the bird sound monitoring data obtained in the step 1, and taking the power value as a lower limit value of a binarization detection threshold of each frequency point
In the formula, k L The invention represents the frequency point sequence number corresponding to the upper side frequency of the low frequency band with highly concentrated environmental noise energy, and k is taken L 20, for 200 Hz;
step 3-2, calculating the minimum signal-to-noise ratio gamma required by satisfying the bird sound detection in each frequency point by using the narrow-band noise average power acquired in step 2 1 The corresponding power value is taken as the candidate value of the binaryzation detection threshold of each frequency point
η 1 (k)＝γ 1 ×Q n (k),1≤k≤K
The invention takes gamma 1 When the value is 4, that is, the bird song SNR at the time frequency point determined to be 1 must be greater than 4;
step 3-3, comparing the threshold candidate value of each frequency point obtained in the step 3-2 with the threshold lower limit value obtained in the step 3-1, and taking the maximum value of the two as the final binary detection threshold of each frequency point
η(k)＝max{η 1 (k),η min },1≤k≤K
Step 4, respectively carrying out 0-1 judgment processing on the power of the frequency points when the bird sound monitoring data in each frequency point is carried out by utilizing the time-frequency power spectrum of the bird sound monitoring data obtained in the step 1 and the binarization detection threshold obtained in the step 3, and then respectively counting the number of the frequency points when the power in each sub-band is higher than the value 1 of the detection threshold:
step 4-1, performing 0-1 judgment processing on the time frequency point power of the bird sound monitoring data in each frequency point to obtain a binaryzation time frequency power spectrum of the bird sound monitoring data in an analysis time period
Step 4-2, counting the number of 1-hour frequency points in each sub-frequency band for the binaryzation time-frequency power spectrum of the bird sound monitoring data obtained in the step 4-1 in the analysis time period
In the formula, I is the number of adjacent sub-bands in the analysis bandwidth, and the invention takes I as 8, and the sub-bands have the same bandwidth; theta i Is the frequency point sequence number set in the ith sub-band
θ i ＝{k|(i-1)B≤(k-1)Δf＜iB},1≤i≤I
In the formula, B and Δ f are respectively the bandwidth of each sub-band and the bandwidth of each frequency point, and in the present invention, B is 1kHz and Δ f is 10 Hz.
step 5-1, normalizing the number of the 1-time frequency points in each sub-band obtained in the step 4 to obtain the distribution ratio of the number of the 1-time frequency points in each sub-band
Step 5-2, performing Shannon index calculation on the distribution ratio of the number of the time frequency points with the value of 1 in each sub-band obtained in the step 5-1 to obtain the numerical value of FADI
Fig. 2 (a) and (b) are a time domain waveform diagram and a corresponding time frequency spectrum diagram of the monitored recording data with the same time frequency distribution of the birdsong signal when the SNR is 5dB and 35dB, respectively. Fig. 3 is a graph showing the change of index values calculated by FADI and ADI respectively for monitoring recording data having the same time-frequency distribution of a birdsong signal, as the SNR of the birdsong, wherein the time length of analysis data for calculating the index under each SNR condition is 10 min. It can be seen from fig. 3 that, when the bird song signals have the same time-frequency distribution, the calculation result of the FADI does not change significantly even when the SNR is as low as 0dB, but the calculation result of the ADI shows significantly monotonically decreasing change with the decrease of the SNR, which indicates that the FADI effectively solves the principle defect of the ADI, thereby reducing the sensitivity of the actual calculation result on the influence of noise. At the same time, FADI values remain consistent with ADI at high SNR, without affecting the ecological interpretation and findings of prior ADI studies.
The invention has convenient realization process, can effectively solve the problem that the existing acoustic index is easily influenced by noise, ensures that the actual calculation value of the FADI has no obvious change when the bird song signal-to-noise ratio is reduced to 0dB when the bird song signal has the same time-frequency distribution, and simultaneously can keep the value consistent with the existing ADI when the bird song signal-to-noise ratio is high, thereby effectively solving the principle defect that the ADI is very sensitive to the influence of the noise, simultaneously being completely compatible with the ecological explanation and discovery of the existing ADI research, providing a new tool for realizing the stable and reliable rapid evaluation of the variety of the birds, and having very important significance on the acoustic monitoring of the ecological environment.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. An avian acoustic diversity index method with low susceptibility to noise influence is characterized by comprising the following steps:
step 1, carrying out short-time Fourier transform on field bird sound monitoring data in an analysis period to obtain a time-frequency power spectrum thereof;
step 2, estimating the average power of the narrow-band noise in each frequency point by using the time-frequency power spectrum of the bird sound monitoring data acquired in the step 1;
step 3, respectively calculating the binarization detection threshold of each frequency point by using the bird sound monitoring data time-frequency power spectrum obtained in the step 1 and the narrow-band noise average power obtained in the step 2;
step 4, respectively carrying out 0-1 judgment processing on the power of the frequency points when the bird sound monitoring data in each frequency point is carried out by utilizing the time-frequency power spectrum of the bird sound monitoring data obtained in the step 1 and the binarization detection threshold obtained in the step 3, and then respectively counting the number of the frequency points when the power in each sub-band is higher than the value 1 of the detection threshold;
and 5, performing Shannon index calculation by using the number of the time-frequency points with the value of 1 in each sub-frequency band obtained in the step 4 to obtain an acoustic diversity index based on frequency variation threshold detection.
2. The method for indexing bird acoustic diversity with low susceptibility to noise influence according to claim 1, wherein step 1 performs short-time fourier transform on the field bird sound monitoring data in the analysis period to obtain the time-frequency power spectrum thereof, specifically as follows:
step 1-1, performing frame processing on field birdsound monitoring data acquired in an analysis period, wherein the frame shift is one frame length; adding a Hanning window to each frame of data, and then performing discrete Fourier transform to obtain S (K, L), wherein K is more than or equal to 1 and less than or equal to K, and L is more than or equal to 1 and less than or equal to L, wherein K and L are frequency point numbers and frame numbers respectively, K is the number of frequency points in an analysis bandwidth, and L is the number of frames in an analysis period;
step 1-2, calculating a time-frequency power spectrum of the field bird sound monitoring data in an analysis time period by using the short-time Fourier transform S (k, l) obtained in the step 1-1
P s (k,l)＝|S(k,l)| 2 ,1≤k≤K,1≤l≤L。
3. The method for bird acoustic diversity index with low susceptibility to noise influence according to claim 1, wherein step 2 estimates the average power of narrow-band noise in each frequency point by using the time-frequency power spectrum of bird acoustic monitoring data acquired in step 1, as follows:
step 2-1, all time frequency points without bird song signals are taken as noise time frequency points, and a noise time frequency point frame number set corresponding to the kth frequency point is recorded as xi k Set xi k The number of the time frequency points of the noise in the middle is M k ；
Step 2-2, obtaining each frequency according to step 2-1Noise time frequency point frame number set xi in point k Respectively calculating the average power of the narrow-band noise in each frequency point
4. The method for bird acoustic diversity index with low susceptibility to noise influence according to claim 1, wherein step 3 calculates the binarization detection threshold of each frequency point by using the bird acoustic monitoring data time-frequency power spectrum obtained in step 1 and the narrow-band noise average power obtained in step 2, specifically as follows:
step 3-1, calculating a power value corresponding to a-50 dBFS detection threshold of ADI by using the time-frequency power spectrum of the bird sound monitoring data obtained in the step 1, and taking the power value as a lower limit value of a binarization detection threshold of each frequency point
In the formula, k L The frequency point sequence number corresponding to the upper side frequency of the low-frequency band with highly concentrated environmental noise energy is represented;
step 3-2, calculating the minimum signal-to-noise ratio gamma required by satisfying the bird sound detection in each frequency point by using the narrow-band noise average power acquired in step 2 1 The corresponding power value is used as the candidate value of the binarization detection threshold of each frequency point
η 1 (k)＝γ 1 ×Q n (k),1≤k≤K
Step 3-3, comparing the threshold candidate value of each frequency point obtained in the step 3-2 with the threshold lower limit value obtained in the step 3-1, and taking the maximum value of the two as the final binary detection threshold of each frequency point
η(k)＝max{η 1 (k),η min },1≤k≤K。
5. The method for indexing the acoustic diversity of birds with reduced susceptibility to noise according to claim 4,characterized in that k is L 20, corresponding to 200 Hz.
6. The method for bird acoustic diversity index with low susceptibility to noise influence according to claim 1, wherein step 4 uses the time-frequency power spectrum of the bird sound monitoring data obtained in step 1 and the binarization detection threshold obtained in step 3 to perform decision processing of 0-1 on the power of the frequency points of the bird sound monitoring data in each frequency point, and then counts the number of the frequency points with the power in each sub-band higher than the detection threshold value 1, specifically as follows:
step 4-1, performing 0-1 judgment processing on the time frequency point power of the bird sound monitoring data in each frequency point to obtain a binaryzation time frequency power spectrum of the bird sound monitoring data in an analysis time period
Step 4-2, counting the number of 1-hour frequency points in each sub-frequency band for the binaryzation time-frequency power spectrum of the bird sound monitoring data obtained in the step 4-1 in the analysis time period
Where I is the number of contiguous subbands in the analysis bandwidth, θ i Is the frequency point sequence number set in the ith sub-band
θ i ＝{k|(i-1)B≤(k-1)Δf＜iB},1≤i≤I
In the formula, B and Δ f are the bandwidth of each sub-band and the bandwidth of each frequency point, respectively.
7. The method for the acoustic diversity index of birds with low susceptibility to noise influence according to claim 1, wherein step 5 uses the number of time-frequency points with value 1 in each sub-band obtained in step 4 to perform shannon index calculation to obtain the acoustic diversity index FADI based on frequency-dependent threshold detection, which is specifically as follows:
step 5-1, normalizing the number of the 1-time frequency points in each sub-band obtained in the step 4 to obtain the distribution ratio of the number of the 1-time frequency points in each sub-band
Step 5-2, performing Shannon index calculation on the distribution proportion of the number of the time frequency points of the value 1 obtained in the step 5-1 in each sub-band to obtain the numerical value of FADI
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1-7 are implemented when the program is executed by the processor.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1-7 when executed by a processor.
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