A method, program, device and storage medium for automatically identifying cetacean whistles

Abstract

The present invention belongs to the technical field of marine biological sound detection, and specifically relates to a method, program, device and storage medium for automatically identifying cetacean whistles. The present invention adopts a time-domain envelope detection method based on summation to eliminate noise signals and prevent impulse noise from affecting detection; adopts a double-threshold endpoint detection algorithm to detect the endpoints of the signal and extract signal segments; utilizes the characteristics that the power spectrum of the CW signal has only one main spectrum line, and the characteristics that the sliding window instantaneous frequency of the LFM signal maintains a monotonically increasing state over time to eliminate CW or LFM signals, and finally uses the surface GM-PHD spectrum profile extraction to determine whether it is a cetacean whistle. The present invention can reduce the workload of manual listening to eliminate interference from non-cetacean whistles and improve the accuracy and work efficiency of cetacean whistle recognition.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine biological sound detection, and in particular relates to a method, program, device and storage medium for automatically identifying cetacean whistles. Background Art

[0002] The ocean is home to many organisms, which use sound to communicate, locate prey, avoid predators, and perform other behaviors. The characteristics of these sounds vary depending on the species, behavior, environment, and seasonality. Marine calls are of great significance in the marine environment, reflecting the distribution, activity patterns, and ecological status of biological populations. Therefore, analyzing these sounds can provide information about biodiversity and the health of marine ecosystems. In recent years, with advances in underwater acoustic technology and increased computing power, the technology for detecting marine calls has made significant progress. From simple underwater sound recording to complex automatic sound recognition systems, scientists are able to more accurately capture, record, and analyze marine sounds. However, the detection and recognition of whistles of cetaceans and other animals often suffers from interference from underwater acoustic signals emitted by other platforms and ambient ocean noise. Therefore, distinguishing cetacean whistles from various detected underwater acoustic signals and background ocean noise facilitates statistical analysis of cetacean characteristics.

[0003] Chinese patent CN117275491B discloses a call classification method based on audio conversion and a temporal attention graph neural network. This method primarily identifies cetacean calls using a neural network classification method. Currently, published domestic patents lack the ability to detect cetacean calls in the presence of interference from underwater acoustic communication signals. Therefore, the design of an automatic cetacean whistle recognition method and the statistical analysis of cetacean characteristics are of great research value. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, program, device and storage medium for automatically identifying cetacean whistles, which can identify and detect cetacean whistles in the presence of underwater acoustic communication signals and marine environmental background noise.

[0005] A method for automatically identifying cetacean whistles comprises the following steps:

[0006] Step 1: Obtain the signal to be detected and use the sum-based time domain envelope detection method to eliminate noise signals; detect the endpoints of the signal and extract the signal segments;

[0007] Step 2: Calculate the power spectrum and use the fact that the CW signal power spectrum has only one main spectral line to identify and exclude the CW signal fragments;

[0008] Step 3: Calculate the instantaneous frequency increment ratio to identify and exclude LFM signal segments;

[0009] Step 4: Extract multiple sets of instantaneous frequencies from the signal segment using the GM-PHD method. Use a polynomial fitting method to obtain fitted values ​​of the instantaneous frequencies. Calculate the square mean of the distance between each set of instantaneous frequencies and the fitted values, and select the largest square mean as the eigenvalue. If the eigenvalue is less than the detection threshold, the signal segment is identified as a cetacean whistle.

[0010] Furthermore, in step 1, the noise signal is eliminated by using a time domain envelope detection method based on summation, specifically:

[0011] Add a window to the detected signal and perform short-time FFT, and take the sum of the maximum values ​​of the short-time FFT of n sliding windows S sum As the test statistic, the calculation formula is:

[0012]

[0013] Among them, n is the total number, S m (t) is the short-time FFT maximum value corresponding to time t, and N is the number of summations;

[0014] The detection statistic is compared with a preset signal detection threshold. If the detection statistic is less than the threshold, it is determined to be a noise signal.

[0015] Furthermore, in step 1, a dual-threshold endpoint detection algorithm is used to detect the endpoints of the signal to obtain the sample point positions in the signal segment that are greater than the first threshold; for each sample point position greater than the threshold, the position is used as the endpoint and the search is continued downward to a position less than the second threshold as another endpoint, the time difference between the two endpoints is calculated as the detected signal duration, and the signal segment between the two endpoints is extracted.

[0016] Furthermore, the calculation method of the instantaneous frequency increment ratio in step 3 is:

[0017] Perform short-time Fourier transform on the signal segment and calculate the instantaneous frequency of the sliding window. Differ the M instantaneous frequencies obtained to obtain M-1 frequency differences. The instantaneous frequency increment ratio f m The calculation formula is:

[0018]

[0019] Among them, f d Indicates the instantaneous frequency corresponding to the dth sliding window; the sliding window instantaneous frequency of the LFM signal remains monotonically increasing over time. If it is an LFM signal, the N-1 instantaneous frequency self-increase ratios of the signal segment are monotonically changing; otherwise, it is not an LFM signal.

[0020] Furthermore, the step 4 is specifically as follows:

[0021] The instantaneous frequency f at a certain moment is regarded as the target to be tracked, and the GM-PHD filter is used to track the spectrum profile. The state vector x of the spectrum profile is k It is composed of the instantaneous frequency f and the rate of change of frequency with time α, that is, x k =[f,α] T , then the state model is:

[0022]

[0023] Where Δ represents the step size of the short-time Fourier transform; v k ~N(0,Σ v ) means the mean is zero and the covariance is Σ v Gaussian noise; in the process of extracting the spectrum profile of cetacean whistle signals, only the frequency quantity is useful information, so the observation model is:

[0024] f k =Hx k +v k

[0025] Wherein, the measurement matrix H = [1 0];

[0026] For each extracted instantaneous frequency f of the signal segment k Using the polynomial fitting method, the fitting value is f k ′, then calculate the instantaneous frequency f k and the fitted value f k ′The square mean of the distance, select the largest square mean as the eigenvalue;

[0027]

[0028] Where Q is the instantaneous frequency f in the spectrum profile extracted from the signal segment k The total number of .

[0029] A computer device / apparatus / system comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned method for automatically identifying cetacean whistles.

[0030] A computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for automatically identifying cetacean whistles.

[0031] A computer program product comprising a computer program / instructions which, when executed by a processor, implement the steps of the above-mentioned method for automatically identifying cetacean whistles.

[0032] The beneficial effects of the present invention are:

[0033] The present invention provides a comprehensive process for identifying cetacean whistle interference from received signals. It uses a sum-based time-domain envelope detection method to eliminate noise signals and prevent impulse noise from affecting detection. A dual-threshold endpoint detection algorithm is used to detect signal endpoints and extract signal segments. CW or LFM signals are excluded by exploiting the characteristics of a CW signal's power spectrum having only one main line and the fact that the sliding window instantaneous frequency of an LFM signal remains monotonically increasing over time. Finally, GM-PHD spectrum profile extraction is used to determine whether the signal is a cetacean whistle. This invention reduces the workload of manually eliminating non-cetacean whistle interference through listening, improving the accuracy and efficiency of cetacean whistle identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a method for automatically identifying cetacean whistles in the present invention.

[0035] Figure 2 is a time-frequency diagram of the signal set to be detected in an embodiment of the present invention.

[0036] Figure 3 These are four whistle spectrum contour maps extracted using the GM-PHD method in an embodiment of the present invention.

[0037] Figure 4 This is a recognition result diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described below with reference to the accompanying drawings.

[0039] The present invention identifies and detects cetacean whistles in the presence of underwater acoustic communication signals and ocean environment background noise, and provides a method, program, device and storage medium for automatic cetacean whistle recognition, which is conducive to improving the accuracy and work efficiency of cetacean whistle recognition and realizing a complete and automatic cetacean call signal recognition process.

[0040] A method for automatically identifying cetacean whistles comprises the following steps:

[0041] Step 1: Obtain the signal to be detected and use the sum-based time domain envelope detection method to eliminate noise signals; detect the endpoints of the signal and extract the signal segments;

[0042] Add a window to the detected signal and perform short-time FFT, and take the sum of the maximum values ​​of the short-time FFT of n sliding windows S sum As the test statistic, the calculation formula is:

[0043]

[0044] Among them, n is the total number, S m (t) is the short-time FFT maximum value corresponding to time t, and N is the number of summations;

[0045] Compare the detection statistic with the preset signal detection threshold. If the detection statistic is less than the threshold, it is determined to be a noise signal.

[0046] A double-threshold endpoint detection algorithm is used to detect the endpoints of the signal and obtain the sample point positions in the signal segment that are greater than the first threshold. For each sample point position greater than the threshold, the position is used as the endpoint to continue downward to a position less than the second threshold as the other endpoint. The time difference between the two endpoints is calculated as the detected signal duration, and the signal segment between the two endpoints is extracted.

[0047] Step 2: Calculate the power spectrum and use the fact that the CW signal power spectrum has only one main spectral line to identify and exclude the CW signal fragments;

[0048] Step 3: Calculate the instantaneous frequency increment ratio to identify and exclude LFM signal segments;

[0049] Perform short-time Fourier transform on the signal segment and calculate the instantaneous frequency of the sliding window. Differ the M instantaneous frequencies obtained to obtain M-1 frequency differences. The instantaneous frequency increment ratio f m The calculation formula is:

[0050]

[0051] Among them, f d Indicates the instantaneous frequency corresponding to the dth sliding window; the sliding window instantaneous frequency of the LFM signal remains monotonically increasing over time. If it is an LFM signal, the N-1 instantaneous frequency increment ratios of the signal segment are monotonically changing; otherwise, it is not an LFM signal;

[0052] Step 4: Extract multiple groups of instantaneous frequencies from the signal segment using the GM-PHD (Gaussian Mixture Probability Hypothesis Density Filter) method. Obtain fitting values ​​for the instantaneous frequencies using a polynomial fitting method. Calculate the square mean of the distances between each group of instantaneous frequencies and the fitting values, and select the largest square mean as the eigenvalue. If the eigenvalue is less than the detection threshold, the signal segment is identified as a cetacean whistle.

[0053] The instantaneous frequency f at a certain moment is regarded as the target to be tracked, and the GM-PHD filter is used to track the spectrum profile. The state vector x of the spectrum profile is k It is composed of the instantaneous frequency f and the rate of change of frequency with time α, that is, x k =[f,α] T , then the state model is:

[0054]

[0055] Where Δ represents the step size of the short-time Fourier transform; v k ~N(0,Σ v ) means the mean is zero and the covariance is Σ v Gaussian noise; in the process of extracting the spectrum profile of cetacean whistle signals, only the frequency quantity is useful information, so the observation model is:

[0056] f k =Hx k +v k

[0057] Wherein, the measurement matrix H = [1 0];

[0058] For each extracted instantaneous frequency f of the signal segment k Using the polynomial fitting method, the fitting value is f k ′, then calculate the instantaneous frequency f k and the fitted value f k ′The square mean of the distance, select the largest square mean as the eigenvalue;

[0059]

[0060] Where Q is the instantaneous frequency f in the spectrum profile extracted from the signal segment k The total number of .

[0061] Example 1:

[0062] The following example uses a signal composed of PSK, FSK, OFDM, DSSS, ship radiation noise, broadband signal, CW, LFM, and four segments of cetacean calls.

[0063] (1) Sampling frequency 48000 Hz, communication frequency band 6-10 kHz, CW signal frequency 8 kHz, LFM frequency modulation slope 6 kHz, ship noise duration 4 s, four different cetacean sound segments.

[0064] (2) Detect whether there is a signal. Using the sum-based time domain envelope detection method, the signal detection statistics are determined and compared with the threshold. The sum detection method improves the classic short-time Fourier transform detection method. The specific improvement idea is: add a window to the received signal and perform a short-time FFT, and take the sum of the maximum values ​​of the n sliding window short-time FFTs S sumAs new detection statistics, 9 different detection statistics are obtained as follows: 148, 75, 178, 182, 209, 77, 77, 74, 73. The double-threshold endpoint detection algorithm is used to detect the endpoints of the signal. The position of the i-th sample point in the signal segment that is greater than the threshold can be obtained. From this position as the starting point, continue to search downward to the position where the value is less than the threshold. The time difference between the two endpoints is calculated as the detected signal duration t i .

[0065] (3) CW signal identification: Calculate the power spectrum and use the fact that the CW signal power spectrum has only one main spectral line to identify the CW signal.

[0066] (4) LFM signal identification. The concept of instantaneous frequency increment ratio is used to identify linear frequency modulation signals. If it is an LFM signal, the N-1 instantaneous frequency increment ratios of this signal should change monotonically, otherwise it is not an LFM signal.

[0067] (5) Extract the instantaneous frequency of the signal based on the GM-PHD (Gaussian mixture probability hypothesis density filter) method. For each extracted instantaneous frequency, a polynomial fitting method is used, where the number of polynomial fittings is 7, and then the square mean of the distance between the instantaneous frequency and the fitting value is calculated, and the largest square mean is selected as the eigenvalue. The eigenvalue of each segment is obtained as follows Figure 4 As shown, the eigenvalues ​​of the first four segments are all greater than 0.001, which is greater than the threshold Th. Therefore, they are identified as underwater acoustic communication signals, and the starting time and duration are marked for subsequent underwater acoustic signal identification. The middle four segments do not have eigenvalues ​​because they are noise or CW, LFM, and other signals, and were pre-excluded, so spectral profile extraction and eigenvalue calculation were not performed. The eigenvalues ​​of the last four segments are all less than 0.001, so they are identified as biological calls and marked. These results are consistent with the generated signal type, which basically confirms the correctness of the automatic cetacean whistle identification process.

[0068] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for automatically identifying cetacean whistles, characterized in that: The following steps are involved: Step 1: Obtain the signal to be detected and use the time domain envelope detection method based on summation to eliminate noise signals; Detect the endpoints of the signal and extract the signal fragments; Step 2: Calculate the power spectrum and use the fact that the CW signal power spectrum has only one main spectral line to identify and exclude the CW signal fragments; Step 3: Calculate the instantaneous frequency increment ratio to identify and exclude LFM signal segments; Step 4: Extract multiple groups of instantaneous frequencies of the signal segments based on the GM-PHD method, obtain the fitting values ​​of the instantaneous frequencies using the polynomial fitting method, calculate the square mean of the distance between each group of instantaneous frequencies and the fitting values, and select the largest square mean as the eigenvalue; If the eigenvalue is less than the detection threshold, the signal segment is identified as a cetacean whistle.

2. The method for automatically identifying cetacean whistles according to claim 1, wherein: In step 1, the noise signal is eliminated by using a time domain envelope detection method based on summation, specifically: Add a window to the detected signal and perform short-time FFT, and take the sum of the maximum values ​​of the short-time FFT of n sliding windows S sum As the test statistic, the calculation formula is: Among them, n is the total number, S m (t) is the short-time FFT maximum value corresponding to time t, and N is the number of summations; The detection statistic is compared with a preset signal detection threshold. If the detection statistic is less than the threshold, it is determined to be a noise signal.

3. The method for automatically identifying cetacean whistles according to claim 1, wherein: In step 1, a dual-threshold endpoint detection algorithm is used to detect the endpoints of the signal to obtain the sample point positions in the signal segment that are greater than the first threshold. For each sample point position greater than the threshold, the position is used as the endpoint to continue downward to a position less than the second threshold as the other endpoint. The time difference between the two endpoints is calculated as the detected signal duration, and the signal segment between the two endpoints is extracted.

4. The method for automatically identifying cetacean whistles according to claim 1, wherein: The calculation method of the instantaneous frequency increment ratio in step 3 is: Perform short-time Fourier transform on the signal segment and calculate the instantaneous frequency of the sliding window. Differ the M instantaneous frequencies obtained to obtain M-1 frequency differences. The instantaneous frequency increment ratio f m The calculation formula is: Among them, f d Indicates the instantaneous frequency corresponding to the dth sliding window; the sliding window instantaneous frequency of the LFM signal remains monotonically increasing over time. If it is an LFM signal, the N-1 instantaneous frequency self-increase ratios of the signal segment are monotonically changing; otherwise, it is not an LFM signal.

5. The method for automatically identifying cetacean whistles according to claim 1, wherein: The step 4 is specifically as follows: The instantaneous frequency f at a certain moment is regarded as the target to be tracked, and the GM-PHD filter is used to track the spectrum profile. The state vector x of the spectrum profile is k It is composed of the instantaneous frequency f and the rate of change of frequency with time α, that is, x k =[f,α] T , then the state model is: Where Δ represents the step size of the short-time Fourier transform; v k ~N(0,Σ v ) means the mean is zero and the covariance is Σ v Gaussian noise; in the process of extracting the spectrum profile of cetacean whistle signals, only the frequency quantity is useful information, so the observation model is: f k =Hx k +v k Wherein, the measurement matrix H = [1 0]; For each extracted instantaneous frequency f of the signal segment k Using the polynomial fitting method, the fitting value is f k ′, then calculate the instantaneous frequency f k and the fitted value f k ′The square mean of the distance, select the largest square mean as the eigenvalue; Where Q is the instantaneous frequency f in the spectrum profile extracted from the signal segment k The total number of .

6. A computer device / apparatus / system comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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