Bio - auditory Inspired Sound Pulse Coding and Recognition Method and System

Abstract

The present invention discloses a method and system for sound encoding and recognition inspired by biological audition. First, a bionic auditory sensor chip based on biological auditory receptors is constructed; then, a bionic auditory encoding model is constructed according to the electrophysiological response patterns of biological auditory receptors under sound stimuli of different frequencies and intensities; finally, the sound to be recognized is encoded into pulse information using this encoding model, and the encoded pulse signal is processed by a pulse neural network to finally achieve sound recognition. The present invention simulates the auditory perception process of mammals and the way of information processing of mammals, and constructs a pulse encoding and sound recognition algorithm with both biological interpretability and efficiency by making full use of the encoding of sound information by organisms. Compared with existing sound recognition methods, the sound recognition of the present invention is more accurate and requires less computing resources.

Description

Technical Field

[0001] The present invention belongs to the cross - field of bio - inspired computing and bionic sensors, and particularly relates to a method and system for sound pulse encoding and recognition inspired by biological audition. Background Art

[0002] Artificial Neural Networks (ANNs) have been widely used in sound recognition tasks. However, the energy requirement of ANNs is huge. Spiking Neural Networks (SNNs), as the third - generation artificial neural networks, process information in the form of spikes. The information in spike form is represented as discrete events at time points. SNNs transform continuous information into discrete spike events through spike encoding and perform calculations only when spikes occur, thus greatly reducing energy consumption. SNNs can not only significantly reduce the system power consumption but also be closer to the information - processing mode of the biological nervous system. How to reasonably encode sound information into spike signals is a prerequisite for SNNs to have good performance.

[0003] Currently, the main spike - encoding methods for sound information are rate encoding and time encoding. Rate encoding means that the firing frequency of spikes is proportional to the intensity of the sound signal. Time encoding means that the delay time of spike firing is used to reflect the energy characteristics of the sound, and the stronger the energy, the earlier the spike firing time. However, these encoding methods have a large gap with real biological audition and have great deficiencies in biological interpretation, and it is difficult to truly reflect the complex encoding mechanism in the biological auditory process. Therefore, how to design a reasonable spike - encoding method that is both low - power - consuming and has biological interpretability has become a key problem restricting the further development of SNNs in the field of sound recognition. In the process of biological audition, sound is conducted through the outer ear and middle ear to the inner ear. Especially in the organ of Corti in the cochlea, the organ of Corti is the primary auditory receptor, which is composed of hair cells, supporting cells, and spiral ganglion cells. Among them, hair cells are the main auditory receptors. Sound stimulation will cause the cilia on the hair cells to undergo mechanical displacement, thereby triggering the opening or closing of mechanically sensitive ion channels on the cell membrane, causing changes in the membrane potential and triggering the generation of action potentials, realizing the conversion of sound signals into nerve signals. This process not only involves the encoding of frequency and intensity but also integrates information in the time and space dimensions, thus achieving efficient and fine - grained sound processing. So far, no complete system has been able to directly extract inspiration from biological auditory responses to construct a complete and reasonable spike - encoding method. Summary of the Invention

[0004] The purpose of the present invention is to propose a method and system for sound pulse encoding and recognition inspired by biological audition in view of the deficiencies of the prior art.

[0005] The purpose of the present invention is achieved by the following technical solutions: A method for sound pulse encoding and recognition inspired by biological audition includes the following steps:

[0006] (1) A biological auditory receptor is used as a sensitive element, and a bionic auditory sensing chip is obtained by configuring a multi-electrode array and an optimized chamber structure, which is used to record the electrophysiological response under sound stimulation;

[0007] (2) The bionic auditory sensing chip is used to detect the response patterns under pure tone stimuli of different frequencies and intensities;

[0008] (3) An optimization algorithm is used to adjust the parameters of the neuron model. Based on the neuron model, the output of the impulse firing patterns generated at different frequencies and intensities is fitted with the response data recorded by the bionic auditory sensing chip to construct a bionic auditory coding algorithm. By preprocessing and feature extracting the sound signal, the obtained sound frequency and intensity information are used as the input of the bionic auditory coding algorithm, and the encoded impulse output is obtained, realizing the encoding of continuous sound signals into discrete impulse signals;

[0009] (4) A sound recognition network based on a spiking neural network is constructed, and the impulse signal obtained in step (3) is input into the network to realize the recognition of sound.

[0010] Further, the biological auditory receptor is cells or tissues extracted from the biological auditory system, including any one of the Organ of Corti, cochlea, inner ear organoid, hair cell or spiral ganglion cell. The biological auditory receptor is cultured on the surface of the multi-electrode array, and the biological auditory receptor is attached to the electrode surface.

[0011] Further, the bionic auditory sensing chip includes a biological auditory receptor, a multi-electrode array and an optimized-size chamber, wherein the optimal size of the chamber is determined by acoustic simulation or experimental feedback to provide the liquid ion environment and ideal acoustic conditions required by the biological auditory receptor; the biological auditory receptor can generate extracellular action potentials under sound stimulation, and the corresponding potential signals are collected through the multi-electrode array to reflect the response pattern of the biological auditory receptor to acoustic stimulation.

[0012] Further, the response pattern is one or more of the firing rate, interspike interval and synchrony.

[0013] Further, the sound stimulation is a pure tone stimulation, and its frequencies include 100 Hz, 500 Hz, 1000 Hz, 2000 Hz, 3000 Hz and 4000 Hz, and the intensities include 60 dB, 70 dB, 80 dB and 90 dB.

[0014] Further, in step (2), pure tones with different frequencies but the same intensity are used to stimulate the bionic auditory sensing chip to test its response to sound frequencies, and pure tones with different intensities but the same frequency are used to stimulate the bionic auditory sensor chip to test its response to sound intensities.

[0015] Further, in step (3), the continuous sound signal is preprocessed, including voice activity detection, pre-emphasis, and constant-Q transform operations, to extract the frequency, intensity, and spectral features of the sound.

[0016] Further, the bionic auditory coding algorithm is based on the Izhikevich neuron model, and the parameters are adjusted by an annealing algorithm, a genetic algorithm, an ant colony algorithm, or a grid search optimization algorithm to minimize the root mean square error between the pulse firing pattern output by the model and the response data recorded by the bionic auditory sensing chip.

[0017] Further, the sound recognition network adopts a three-layer structure of an input layer, an intermediate layer, and an output layer based on a spiking neural network, where the spiking neuron is a LIF neuron, and the gradient replacement method is used to achieve backpropagation during the training process.

[0018] On the other hand, the present invention also provides a sound pulse coding and recognition system inspired by biological audition, which includes:

[0019] A bionic auditory sensing chip construction module, which is used to use a biological auditory receptor as a sensitive element, and is configured with a multi-electrode array and an optimized chamber to construct a bionic auditory sensing chip;

[0020] A bionic auditory response module, which is used to use a variety of sound stimuli to test the response patterns of the bionic auditory sensing chip to pure tones of different frequencies and different intensities;

[0021] A sound preprocessing and feature extraction module, which is used to preprocess and extract features from the sound signal to obtain the sound frequency and intensity information;

[0022] A bionic auditory coding module, which is used to fit the pulse firing patterns generated at different frequencies and intensities with the response data recorded by the bionic auditory sensing chip based on a neuron model to construct a bionic auditory coding algorithm; input the sound frequency and intensity information, and encode the continuous sound signal into discrete pulse signals;

[0023] A sound recognition module based on a spiking neural network, which is used to process the encoded pulse signals to achieve accurate sound recognition.

[0024] Advantages of the present invention:

[0025] (1) The method of the present invention simulates the perception process of the biological auditory system and more reflects the biological response to sound;

[0026] (2) Meanwhile, the present invention uses a biological auditory receptor as a sensitive element for auditory perception to construct a bionic auditory sensing chip, so that the auditory response conforming to the biological perception of sound stimuli can be directly obtained;

[0027] (3) The bionic auditory coding algorithm constructed by the present invention encodes sound information into pulse information by directly simulating the sound response pattern obtained in the bionic auditory sensing chip, which is both biologically reasonable and computationally feasible;

[0028] (4) Compared with traditional sound recognition methods, the present invention encodes the sound signal into discrete pulse signals and constructs a pulse neural network to recognize the encoded pulse signals, which is more in line with the biological auditory perception process;

[0029] (5) The present invention proposes a whole bionic auditory perception system from auditory perception, conversion, and recognition, including a bionic auditory sensing chip, bionic auditory coding, and a pulse neural network, and achieves better recognition results in the sound recognition task. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1 It is a schematic diagram of the constructed bionic auditory sensing chip.

[0032] Figure 2 It is a schematic diagram of the electrical signals recorded by the bionic auditory sensing chip under different sound stimuli.

[0033] Figure 3 It is a schematic diagram comparing the output of the bionic auditory coding algorithm with the response pattern of the biological auditory receptor.

[0034] Figure 4 It is a schematic diagram of the coding results and recognition results of the bionic auditory coding algorithm in different datasets. DETAILED DESCRIPTION OF THE INVENTION

[0035] The following further elaborates on the technical solutions of the present invention in conjunction with the drawings. It should be noted that the detailed description is only for the present invention and should not be regarded as a limitation of the present invention.

[0036] The biological auditory sensitive element used in the bionic auditory sensing chip of the present invention is the organ of Corti, but it is not limited to the organ of Corti. It can also be the cochlea, inner ear organoids, hair cells, spiral ganglion cells and other tissues or cells with auditory functions.

[0037] The bionic auditory sensing chip used in the present invention for electrophysiological signal detection is a planar multi-electrode array chip, but it is not limited to the planar multi-electrode array chip. It can also be a patch clamp, a three-dimensional multi-electrode array chip, a flexible multi-electrode array chip and other devices for detecting electrophysiology.

[0038] The optimized chamber used in the present invention is an acrylic chamber optimized by acoustic simulation. However, the optimization method is not limited to acoustic simulation. It can also be optimization methods such as experimental test feedback optimization and multi-physics field coupling simulation. The optimized parameters are not limited to size. They can also be parameters such as the shape of the chamber and the internal structure of the chamber. The material of the chamber is not limited to acrylic. It can also be materials such as PDMS and PCL.

[0039] The sound stimulator used in the present invention is a loudspeaker, but it is not limited to the loudspeaker. It can also be piezoelectric ceramics, MEMS acoustic transducers and other devices and apparatuses for providing acoustic stimulation.

[0040] The bionic auditory coding algorithm constructed in the present invention is constructed by minimizing the root mean square error between the output optimized by the annealing algorithm and the electrophysiological signal (spike) response pattern induced by the sound recorded by the bionic auditory sensing chip. However, the optimization algorithm is not limited to the annealing algorithm. It can also be optimization algorithms such as genetic algorithm, ant colony algorithm and grid search. The optimization objective, that is, the response pattern is not limited to the firing rate of spikes. It can also be response patterns such as interspike interval and synchrony.

[0041] The specific embodiments of the present invention are as follows:

[0042] The bionic auditory sensor chip designed in the present invention is as Figure 1 shown, including a biological auditory receptor as a sensitive element, a multi-electrode array sensor for recording the electrophysiological response of the biological auditory receptor under sound stimulation, and an optimized chamber for providing the ionic environment and acoustic stimulation environment required by the biological auditory receptor;

[0043] The bionic auditory sensing chip is composed of a biological auditory receptor, a multi-electrode array sensor and a chamber. Among them, the chamber is connected to the multi-electrode array sensor through epoxy resin. The biological auditory receptor is cultured on the surface of the multi-electrode array, and the biological auditory receptor is attached to the electrode surface;

[0044] The described biological auditory receptor is used as an auditory sensitive element to sense sound stimuli and generate corresponding spike signals. In this implementation, the Organ of Corti tissue is selected as the auditory sensitive element. The Organ of Corti tissue contains various auditory receptor cells, including hair cells and spiral ganglion cells, which can sense sound stimuli and generate responses.

[0045] The described multi-electrode array sensor is used to detect the electrophysiological signals of the biological auditory receptor induced by sound. It has 64 channels and is connected to a printed circuit board through gold wires. The detected signals are transmitted to a computer through a signal collector and an amplifier.

[0046] The optimized chamber is used to provide the ionic environment and acoustic environment required by the biological auditory receptor. Through an optimization method, the bionic auditory sensing chip is within the sound detection range. The interpolation method is used to map the sound frequency response pattern obtained by the bionic auditory sensing chip to 0 - 4000 Hz, and the response pattern to sound intensity is mapped to 0 - 100 to meet the construction requirements of the bionic auditory coding algorithm. The energy attenuation is the smallest when the sound frequency is in the range of 0 - 4000 Hz and the intensity is in the range of 60 - 90 dB, ensuring that the bionic auditory sensing chip can record stable electrophysiological responses within this range of sound frequency and intensity stimuli. The specific simulated acoustic environment is as follows: A point source is set 5 cm away from the chamber to simulate the sound wave input of a speaker; air and a perfectly matched layer are set outside the chamber to prevent reflection; a 300 - um - thick ionic liquid layer is set at the bottom to simulate the ionic environment required by the auditory sensor, and a 1 - mm × 1 - mm × 0.2 - mm cube in the middle represents the biological auditory sensor. The simulation process includes the generation of sound waves by the speaker, passing through the air, propagating to the solid chamber, and then propagating to the liquid. The acoustic wave transfer equations in air and liquid are as follows:

[0047]

[0048] where p is the sound pressure, t is the time, c is the speed of sound in air in air and the speed of sound in liquid in liquid, is the Laplace operator, representing the characteristics of wave diffusion and propagation in space. The elastic wave equation for sound wave propagation in a solid is:

[0049]

[0050] where, is the fluid density, is the density of the solid, is the fluid sound speed, is the solid sound speed.

[0051] The described biological auditory receptors are cultured on the surface of a multi - electrode array sensor, and the coupling between the tissue and the electrode is improved by modifying polylysine on the electrode surface.

[0052] Use the above - mentioned bionic auditory sensing chip to detect the response patterns under different sound stimuli and construct a bionic auditory coding algorithm according to the response patterns. The specific detection and construction process includes the following steps:

[0053] (1) Detect auditory response: Add 150 μL of artificial lymph fluid into the chamber before each test. Give sound stimuli with different spectra and intensities through a speaker. The sound frequency range is 100, 500, 1000, 2000, 3000, 4000 Hz, and the sound intensity range is pure - tone stimuli of 60, 70, 80, 90 dB. The stimulation time is 30 s, the interval is 30 s, and it is repeated 3 times. Synchronously collect the electrical signals of the bionic auditory sensing chip. All experiments are carried out at room temperature of 25 degrees.

[0054] (2) Auditory response pattern analysis: The signals recorded by the above - mentioned bionic auditory sensing chip under different sound stimuli are as Figure 2 shown. First, filter the original signal. Use a Butterworth second - order high - pass filter to extract the signals above 250 Hz. Further extract the spike signals from the filtered signals. Use the method with a threshold greater than 3 times the standard deviation to extract the effective spike signals, and analyze the firing rate, inter - spike interval, and synchrony of the spike signals as the response pattern under this sound stimulus.

[0055] (3) Construction of bionic auditory coding algorithm: The firing rate of the spike signals under different sound stimuli above is used as the biological basis for constructing the bionic auditory coding algorithm. Use the Izhikevich model as the skeletal model of the bionic auditory coding algorithm, and its formula is as follows:

[0056]

[0057]

[0058] Among them, I represents the sound stimulus, and a, b, c, d represent the parameters of the model. When the membrane voltage exceeds 30, a spike signal is obtained. By statistically analyzing the pulse signals, the response pattern of the bionic auditory coding algorithm is Model_response. The response pattern of the obtained bionic auditory sensing chip to spikes under different sound stimuli is OC_response. The model parameters a, b, c, d are optimized through an optimization algorithm to minimize the root mean square error between Model_response and OC_response, where the response pattern can be one or more of the firing rate, interspike interval, and synchrony, thus obtaining the bionic auditory coding algorithm. Among them, the value of I is mapped to 0 - 100. The optimized bionic auditory coding algorithm and the response of the bionic auditory sensing chip are as Figure 3 shown.

[0059] The above bionic auditory coding algorithm is used to encode the sound signal, making the continuous sound signal into a discrete pulse signal, and a sound recognition algorithm based on a spiking neural network is constructed to decode the pulse signal to achieve efficient sound recognition. The specific construction process includes the following steps:

[0060] (1) Selection of test data: In this implementation, two different data sets are selected for the sound recognition task. The TIDIGITS data set is a publicly available data set of the Language Data Consortium and is one of the most commonly used speech data sets for benchmarking speech recognition algorithms. This data set contains oral digit utterances of 111 male and 114 female speakers. 4,950 isolated digit spoken sentences are selected from it for pulse coding and sound recognition. The data contains 11 isolated digit spoken words (i.e., "zero" to "nine" and "oh"). These isolated digit spoken words are randomly split, with 2,464 used for training and 2,486 used for testing.

[0061] The sound scene data set of the Real World Computing Partnership (RWCP) is recorded in a real acoustic environment. For a fair comparison with the TIDIGITS data set, the same 10 sound event categories in the data set are used: "bells5", "bottle1", "buzzer", "cymbals", "horn", "kara", "metal15", "phone4", "ring", "whistle1". In the present invention, 40 sound segments are randomly selected from each category, with 20 used for training and the remaining 20 used for testing. Therefore, there are 200 training samples and 200 test samples each.

[0062] (2)Preprocessing and Feature Extraction of Sound Data: First, downsample the sound data to 8 kHz. Second, perform voice activity detection in the sound stream, and calculate the maximum energy and spectral centroid of each frame as thresholds. Using these thresholds, frames are regarded as voice activities or silences, and signal segments containing sounds are extracted. The constant-Q transform (CQT) is used to extract the spectral features of the preprocessed sound signal, and its formula is:

[0063]

[0064]

[0065] where represents the calculated energy information of the sound, which is input into the bionic auditory coding algorithm and satisfies .

[0066] (3)Pulse Coding and Recognition of Sound: The sound information after the above preprocessing and feature extraction is input into the bionic auditory coding algorithm to obtain the corresponding pulse coding results. A sound recognition algorithm based on a pulse neural network is constructed to decode the pulse information to obtain the sound recognition results. The pulse neural network in this implementation has three layers, including an input layer, an intermediate layer, and an output layer. The structure of the recognition model for the TIDIGITS dataset is 6889-1024-11, and the structure of the recognition model for the RWCP data is 6889-1024-10. The pulse neurons in the SNN use LIF neurons, and its formula is:

[0067]

[0068] where . Since the pulse is the Heaviside step function in the form of 0 or 1, during the training process, the gradient substitution method is used for training, that is, the step function is used in the forward propagation, and a substitution function with a shape similar to the step function and continuous and differentiable is constructed in the reverse propagation, and its derivative is used for reverse differentiation. The definitions of the substitution function and its derivative are as follows:

[0069]

[0070]

[0071] where 𝛼 is the width of the surrogate gradient, set to 2. During the training process of the pulse neural network, MSELoss is used as the loss function, the AdamW optimizer is used for training, the weight decay regularization coefficient is 0.02, the initial learning rate is 0.01, and the learning rate is dynamically adjusted using ReduceLROnPlateau, epoch = 200, batchsize = 64. The results of its sound coding and recognition are asFigure 4 as shown in Table 1.

[0072] Table 1 Comparison of Recognition Accuracy of Bionic Auditory Coding Algorithm in SNNs with the Same Structure and Traditional Non-SNN Models

[0073]

[0074] Note: 1. The model compares SNNs with the same structure with convolutional neural networks (CNNs) and recurrent neural networks (RNNs);

[0075] 2. The number of parameters represents the number of neurons required by the neural network.

[0076] The features of the present invention are as follows: (1) The present invention constructs a bionic auditory sensing chip with a biological auditory receptor as a sensitive element for auditory perception, which can directly obtain the auditory response after the biological senses a sound stimulus, that is, the present invention can directly obtain the electrophysiological signals of the biological receptor after being stimulated by different sound frequencies and different sound intensities, which more reflects the biological response to sound;

[0077] (2) The bionic auditory coding algorithm constructed by the present invention is highly consistent with the response pattern of the biological auditory receptor to sound, has biological rationality, and based on a computable neuron model, the bionic auditory coding algorithm has computational feasibility and can meet large-scale computational requirements;

[0078] (3) Compared with traditional sound recognition algorithms, the present invention encodes sound signals into discrete pulse signals and constructs a pulse neural network to recognize the encoded pulse signals, which is more in line with the biological auditory perception process. And on the basis of the same model structure, it has fewer parameters, higher accuracy, requires less computing resources, and at the same time, the processing process in the form of pulses more meets the low-power requirements.

[0079] On the other hand, corresponding to the foregoing embodiment of a method for sound pulse coding and recognition based on biological auditory response, the present invention also provides an embodiment of a system for sound pulse coding and recognition based on biological auditory response. The system includes: a bionic auditory response module, a sound preprocessing and feature extraction module, a bionic auditory coding module, and a sound recognition module based on a pulse neural network. For the specific implementation process of each module, please refer to the specific implementation steps of the foregoing embodiment of a method for pulse-driven sound coding and recognition inspired by biological auditory response.

[0080] A bionic auditory sensing chip construction module, which is used to use a biological auditory receptor as a sensitive element and configure a multi-electrode array and an optimized chamber to construct a bionic auditory sensing chip;

[0081] A bionic auditory response module, which is used to obtain the response patterns of biological auditory receptors to pure tones of different frequencies and different intensities, and the response patterns of the organism are used as the construction basis of the bionic auditory coding module;

[0082] A sound preprocessing and feature extraction module, which is used to perform preliminary processing on continuous sounds, including operations such as extraction of the effective segment of the sound and pre-emphasis, and further extract the sound frequency and intensity features;

[0083] A bionic auditory coding module, which is used to use the aforementioned bionic auditory sensing chip to respond to the sound signal pattern, and based on the neuron model, fit the pulse firing pattern generated at different frequencies and intensities with the response data recorded by the bionic auditory sensing chip to construct a bionic auditory coding algorithm; encode the sound features extracted by the sound preprocessing and feature extraction module into pulse signals;

[0084] A sound recognition module based on a spiking neural network, which is used to recognize the sound signals encoded in pulse form.

[0085] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for sound pulse encoding and recognition inspired by biological audition, characterized in that, It includes the following steps: (1) Using a biological auditory receptor as a sensitive element, and configuring a multi-electrode array and an optimized chamber to obtain a bionic auditory sensing chip for recording electrophysiological responses under sound stimulation; (2) Using the bionic auditory sensing chip to detect the response patterns under pure tone stimuli of different frequencies and different intensities; (3) Using an optimization algorithm to adjust the parameters of the neuron model, and fitting the output of the spike train patterns generated at different frequencies and intensities based on the neuron model with the response data recorded by the bionic auditory sensing chip to construct a bionic auditory coding algorithm. By preprocessing and feature extraction of the sound signal, the obtained sound frequency and intensity information are used as the input of the bionic auditory coding algorithm to obtain the encoded spike output, realizing the encoding of continuous sound signals into discrete spike signals; (4) Constructing a sound recognition network based on a spiking neural network, and inputting the spike signal obtained in step (3) into the network to realize the recognition of sound.

2. The method for encoding and recognizing sound pulses inspired by biological audition according to claim 1, wherein The biological auditory receptor is cells or tissues extracted from the biological auditory system, including any one of the organ of Corti, cochlea, inner ear organoid, hair cell or spiral ganglion cell. The biological auditory receptor is cultured on the surface of the multi-electrode array, and the biological auditory receptor fits the electrode surface.

3. The method for bio - auditory - inspired sound pulse coding and recognition according to claim 1, wherein The bionic auditory sensing chip includes a biological auditory receptor, a multi-electrode array, and an optimized-size chamber, where the optimal size of the chamber is determined by acoustic simulation or experimental feedback to provide the liquid ion environment and ideal acoustic conditions required by the biological auditory receptor; the biological auditory receptor can generate extracellular action potentials under sound stimulation, and the corresponding potential signals are collected through the multi-electrode array to reflect the response pattern of the biological auditory receptor to acoustic stimulation.

4. The method for bio-auditory-inspired sound pulse coding and recognition according to claim 1, wherein The response pattern is one or more of the firing rate, inter-spike interval, and synchrony.

5. The method for bio - auditory - inspired sound pulse coding and recognition according to claim 1, wherein The sound stimulation is a pure tone stimulation, and its frequencies include 100 Hz, 500 Hz, 1000 Hz, 2000 Hz, 3000 Hz, and 4000 Hz, and the intensities include 60 dB, 70 dB, 80 dB, and 90 dB.

6. The method for bio - auditory - inspired sound pulse coding and recognition according to claim 1, characterized in that, In step (2), different frequency pure tone stimuli with the same intensity are used to test the response of the bionic auditory sensing chip to the sound frequency, and different intensity pure tone stimuli with the same frequency are used to test the response of the bionic auditory sensor chip to the sound intensity.

7. The method for encoding and recognizing sound pulses inspired by biological audition according to claim 1, characterized in that, In step (3), preprocessing of the continuous sound signal includes voice activity detection, pre-emphasis, and constant-Q transform operations to extract the frequency, intensity, and spectral features of the sound.

8. The method for encoding and recognizing sound pulses inspired by biological audition according to claim 1, characterized in that, The bionic auditory coding algorithm is based on the Izhikevich neuron model, and the parameters are adjusted by an annealing algorithm, a genetic algorithm, an ant colony algorithm, or a grid search optimization algorithm to minimize the root mean square error between the spike train pattern output by the model and the response data recorded by the bionic auditory sensing chip.

9. The method for encoding and recognizing sound pulses inspired by biological audition according to claim 1, characterized in that, The sound recognition network adopts a three-layer structure of an input layer, an intermediate layer, and an output layer based on a spiking neural network, where the spiking neuron is a LIF neuron, and the gradient replacement method is used to realize backpropagation during the training process.

10. A bio - auditory - inspired sound pulse coding and recognition system for implementing the method according to claim 1, characterized in that, The system includes: A bionic auditory sensing chip construction module, which uses a biological auditory receptor as a sensitive element, and is configured with a multi-electrode array and an optimized chamber to construct a bionic auditory sensing chip; A bionic auditory response module, which is used to use a variety of sound stimuli to test the response patterns of the bionic auditory sensing chip to pure tones of different frequencies and different intensities; A sound preprocessing and feature extraction module, which is used to preprocess and extract features from sound signals to obtain sound frequency and intensity information; A bionic auditory coding module, which is used to fit the pulse firing patterns generated at different frequencies and intensities with the response data recorded by the bionic auditory sensing chip based on a neuron model to construct a bionic auditory coding algorithm; input sound frequency and intensity information, and encode continuous sound signals into discrete pulse signals; A sound recognition module based on a spiking neural network, which is used to process the encoded pulse signals to achieve accurate sound recognition.

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