Low-delay zoning pitch detection method, device and equipment based on neural network

Abstract

The invention relates to a low-delay zoning pitch detection method, device and equipment based on a neural network. The method comprises the following steps: acquiring an audio signal; and constructing a pitch detection model based on a neural network, and inputting the audio signal as sample training data into a filter of the pitch detection model for filtering to obtain multiple sub-band audio vectors. And performing feature extraction on each path of sub-band audio vector through a preset number of convolution layers in the neural network to obtain a first pitch sub-band. And manually correcting the audio signal to obtain a second pitch signal. And inputting the first pitch sub-band and the second pitch signal into a pitch detection model for model training to obtain a pre-trained pitch detection model. And the pre-training pitch detection model checks the audio signal according to the loss function and outputs a low-delay pitch signal. By adopting the method, the delay degree of pitch detection can be remarkably reduced, and the real-time performance and the time domain resolution fineness of related processing of various pitches are improved.

Description

Technical Field

[0001] The present invention relates to the field of audio detection technology, and in particular to a low-delay band-based pitch detection method, device and equipment based on a neural network. Background Art

[0002] Pitch detection, a core problem in the field of audio processing and music information retrieval, involves accurately identifying the fundamental frequency of a note or sound from an audio signal. This technology has a wide range of uses in various applications, such as automatic music transcription, music teaching, sound analysis, and vocal practice assistance.

[0003] Early pitch detection methods were mostly based on time domain and frequency domain analysis. Representative algorithms include autocorrelation, fast Fourier transform, and cepstrum. The characteristics of these algorithms are to analyze a whole block of audio data and obtain a pitch estimate of this audio data. Therefore, there is a delay problem because it requires a whole block of data for analysis. In addition, the time domain resolution is not enough because it extracts the features of a block of data.

[0004] In recent years, with the development of machine learning and artificial intelligence, pitch detection technology has also been significantly improved. Deep learning algorithms, especially convolutional neural networks (CNN) and recurrent neural networks (RNN), have been used to improve the accuracy of detection. However, they are computationally complex and often require feature extraction based on spectrum or cepstrum calculations, so they also have the same problem of insufficient real-time performance as the above methods. Summary of the invention

[0005] Based on this, it is necessary to provide a low-delay banded pitch detection method, device and equipment based on a neural network that can reduce the delay of pitch detection in order to address the above technical problems.

[0006] A low-delay band-splitting pitch detection method based on a neural network, the method comprising:

[0007] Get the audio signal.

[0008] A pitch detection model based on a neural network is constructed, and the audio signal is input as sample training data into the filter of the pitch detection model for filtering to obtain multi-channel sub-band audio vectors.

[0009] Each sub-band audio vector is subjected to feature extraction through a preset number of convolutional layers in the neural network to obtain a first pitch sub-band.

[0010] The second pitch signal is obtained by artificially modifying the audio signal.

[0011] The first pitch subband and the second pitch signal are input into a pitch detection model for model training to obtain a pre-trained pitch detection model.

[0012] The pre-trained pitch detection model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal.

[0013] In one of the embodiments, it also includes: constructing a pitch detection model based on a neural network, determining the filter order according to the sampling environment, inputting the audio signal as sample training data into the filter of the corresponding filter order in the pitch detection model for order filtering, and obtaining a multi-channel sub-band audio vector.

[0014] In one of the embodiments, it also includes: each sub-band audio vector is subjected to feature extraction through a preset number of convolutional layers in the LSTM network architecture, and the extracted features are used to determine the probability of the zero-crossing rate of the sub-band audio vector using a softmax function, so as to obtain the sub-band audio vector corresponding to the maximum probability as the first pitch sub-band.

[0015] In one embodiment, the method further includes: calculating the energy characteristics of the sub-band audio vector by using a preset number of convolution layers in the LSTM network architecture:

[0016]

[0017] Wherein, E(n) is the energy feature of the sub-band audio vector, n is the current time point, x is the sub-band audio data, and L is the time window size for calculating energy.

[0018] In one of the embodiments, the method further includes: inputting the first pitch subband and the second pitch signal into a pitch detection model for model parameter training to obtain parameters of a neural network, optimizing the pitch detection model according to the parameters, and obtaining a pre-trained pitch detection model.

[0019] In one embodiment, the method further includes: training a pitch detection model to calibrate the audio signal according to a cross entropy loss function and outputting a low-latency pitch signal:

[0020]

[0021] Among them, Loss is the cross entropy loss function, N is the filter order, y i is the first pitch subband, It is a low-latency pitch signal.

[0022] A low-delay band-splitting pitch detection device based on a neural network, the device comprising:

[0023] The audio signal acquisition module is used to acquire the audio signal.

[0024] The sub-band audio vector acquisition module is used to build a pitch detection model based on a neural network, input the audio signal as sample training data into the filter of the pitch detection model for filtering, and obtain multiple sub-band audio vectors.

[0025] The first pitch sub-band acquisition module is used to extract features of each sub-band audio vector through a preset number of convolution layers in a neural network to obtain a first pitch sub-band.

[0026] The second pitch signal acquisition module is used to obtain a second pitch signal by manually correcting the audio signal.

[0027] The model training module is used to input the first pitch subband and the second pitch signal into the pitch detection model for model training to obtain a pre-trained pitch detection model.

[0028] The low-latency pitch signal acquisition module is used to pre-train the pitch detection model to calibrate the audio signal according to the loss function and output a low-latency pitch signal.

[0029] In one of the embodiments, it also includes: a first pitch subband acquisition module, which is also used to extract features of each sub-band audio vector through a preset number of convolution layers in the LSTM network architecture, and use the softmax function to determine the probability of the zero-crossing rate of the sub-band audio vector using the extracted features, so as to obtain the sub-band audio vector corresponding to the maximum probability as the first pitch sub-band.

[0030] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Get the audio signal.

[0032] A pitch detection model based on a neural network is constructed, and the audio signal is input as sample training data into the filter of the pitch detection model for filtering to obtain multi-channel sub-band audio vectors.

[0033] Each sub-band audio vector is subjected to feature extraction through a preset number of convolutional layers in the neural network to obtain a first pitch sub-band.

[0034] The second pitch signal is obtained by artificially modifying the audio signal.

[0035] The first pitch subband and the second pitch signal are input into a pitch detection model for model training to obtain a pre-trained pitch detection model.

[0036] The pre-trained pitch detection model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0038] Get the audio signal.

[0039] A pitch detection model based on a neural network is constructed, and the audio signal is input as sample training data into the filter of the pitch detection model for filtering to obtain multi-channel sub-band audio vectors.

[0040] Each sub-band audio vector is subjected to feature extraction through a preset number of convolutional layers in the neural network to obtain a first pitch sub-band.

[0041] The second pitch signal is obtained by artificially modifying the audio signal.

[0042] The first pitch subband and the second pitch signal are input into a pitch detection model for model training to obtain a pre-trained pitch detection model.

[0043] The pre-trained pitch detection model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal.

[0044] The above-mentioned low-latency band-based pitch detection method, device and equipment based on neural network firstly filters the audio signal through a filter and decomposes it into multiple sub-band audio vectors. This sub-band processing method carefully analyzes the frequency components of the signal, which helps to improve the accuracy of pitch detection. Then, each sub-band audio vector is subjected to feature extraction through a preset convolution layer in the neural network to obtain a first pitch sub-band. With its powerful feature extraction ability, the convolutional neural network extracts pitch-related features from complex audio signals and improves the detection accuracy. Then, by manually correcting the audio signal, a second pitch signal is obtained to provide accurate pitch annotation, which lays the foundation for supervised learning of the model. The first pitch sub-band and the second pitch signal are input into the pitch detection model for training to obtain a pre-trained pitch detection model. This process combines the features extracted by the convolution layer and the manually corrected pitch signal to improve the accuracy and robustness of the model. Finally, the pre-trained model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal. The cross entropy loss function measures the difference between the prediction output by the model and the actual pitch signal, and optimizes the model performance by minimizing the loss function. Throughout the process, the efficient feature extraction capability of convolutional neural networks, the parallel processing of multi-channel sub-band audio vectors, and the pre-training and proofreading mechanism are utilized to not only improve the accuracy of pitch detection, but also significantly reduce processing delays. The optimized convolutional neural network architecture makes audio signal processing more efficient and reduces computational overhead; the sub-band decomposition method processes multiple frequency band signals in parallel, improves processing efficiency, and reduces delays; combined with the pre-trained model and cross-entropy loss function proofreading, while ensuring accuracy, the model's inference time is shortened, solving the accuracy and low latency issues of pitch detection, and ultimately achieving the technical effect of low-latency pitch detection, which is suitable for real-time audio processing and other application scenarios that require efficient pitch detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 1 is a flow chart of a low-latency band-splitting pitch detection method based on a neural network in one embodiment;

[0046] Figure 2 A schematic diagram of a process of low-latency band-splitting pitch detection based on a neural network in one embodiment;

[0047] Figure 3 A schematic diagram of a process of extracting energy generated by a filter using an LSTM network architecture in one embodiment;

[0048] Figure 4 is a structural block diagram of a low-delay band-splitting pitch detection device based on a neural network in one embodiment;

[0049] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] In one embodiment, Figure 1 As shown, a low-latency band-splitting pitch detection method based on a neural network is provided, comprising the following steps:

[0052] Step 102: Acquire an audio signal.

[0053] Specifically, the audio signal is obtained through a pitch recognition module in a vocal effector.

[0054] Step 104, construct a pitch detection model based on a neural network, input the audio signal as sample training data into the filter of the pitch detection model for filtering, and obtain multi-channel sub-band audio vectors.

[0055] Specifically, 64 filters are constructed, using a 4th-order Butterworth bandpass filter. You can also select filters based on the properties of the audio signal and use other bandpass, lowpass, or highpass filters for band processing. The human voice from 20 to 800 Hz is divided into 64 sub-bands in the log domain, and the width of each sub-band is log(2) / 12. This means that the range of the nth sub-band is:

[0056] Furthermore, a neural network model is selected for feature extraction. This solution uses an LSTM (Long Short-Term Memory) time recurrent neural network, or a dense, RNN or custom network model. The 64 signals filtered out by these 64 filters are used to detect their energy and zero-crossing rate, respectively, to obtain multiple sub-band audio vectors. Since the signal is generally composed of fundamental frequency and harmonics, these 64 filters will show some significant regularity in the vowel segment. In addition, the band filtering can use a common elliptical bandpass filter group, or other bandpass filters can be used.

[0057] Step 106: feature extraction is performed on each sub-band audio vector through a preset number of convolution layers in the neural network to obtain a first pitch sub-band.

[0058] Step 108: Obtain a second pitch signal by manually correcting the audio signal.

[0059] Step 110: Input the first pitch subband and the second pitch signal into a pitch detection model for model training to obtain a pre-trained pitch detection model.

[0060] Step 112: The pre-trained pitch detection model calibrates the audio signal according to the loss function and outputs a low-latency pitch signal.

[0061] Specifically, a cross entropy (categorical_crossentropy) loss function is constructed to optimize the features of the input audio signal, and the output is a 64*1 vector.

[0062]

[0063] Among them, Loss is the cross entropy loss function, N is the filter order, y i is the first pitch subband, For low-latency pitch signals, the loss function type can also be adjusted according to the neural network model, such as Mean Absolute Error (MAE), Log Loss, Smooth L1 Loss, etc.

[0064] In the above-mentioned low-latency band-based pitch detection method based on neural network, first, the audio signal is filtered by a filter and decomposed into multiple sub-band audio vectors. This sub-band processing method carefully analyzes the frequency components of the signal, which helps to improve the accuracy of pitch detection. Then, each sub-band audio vector is subjected to feature extraction through a preset convolution layer in the neural network to obtain a first pitch sub-band. With its powerful feature extraction ability, the convolutional neural network extracts pitch-related features from complex audio signals and improves the detection accuracy. Then, by manually correcting the audio signal, a second pitch signal is obtained to provide accurate pitch annotation, which lays the foundation for supervised learning of the model. The first pitch sub-band and the second pitch signal are input into the pitch detection model for training to obtain a pre-trained pitch detection model. This process combines the features extracted by the convolution layer and the manually corrected pitch signal to improve the accuracy and robustness of the model. Finally, the pre-trained model proofreads the audio signal according to the cross-entropy loss function and outputs a low-latency pitch signal. The cross-entropy loss function measures the difference between the prediction output by the model and the actual pitch signal, and optimizes the model performance by minimizing the loss function. Throughout the process, the efficient feature extraction capability of convolutional neural networks, the parallel processing of multi-channel sub-band audio vectors, and the pre-training and proofreading mechanism are utilized to not only improve the accuracy of pitch detection, but also significantly reduce processing delays. The optimized convolutional neural network architecture makes audio signal processing more efficient and reduces computational overhead; the sub-band decomposition method processes multiple frequency band signals in parallel, improves processing efficiency, and reduces delays; combined with the pre-trained model and cross-entropy loss function proofreading, while ensuring accuracy, the model's inference time is shortened, solving the accuracy and low latency issues of pitch detection, and ultimately achieving the technical effect of low-latency pitch detection, which is suitable for real-time audio processing and other application scenarios that require efficient pitch detection.

[0065] In one of the embodiments, a neural network-based pitch detection model is constructed, the filter order is determined according to the sampling environment, and the audio signal is input as sample training data into the filter of the corresponding filter order in the pitch detection model for step-by-step filtering to obtain multi-channel sub-band audio vectors.

[0066] In one embodiment, if Figure 3 As shown, each sub-band audio vector is feature extracted through the number of convolution layers preset in the LSTM network architecture, and the extracted features are used to determine the probability of the zero-crossing rate of the sub-band audio vector using the softmax function, so as to obtain the sub-band audio vector corresponding to the maximum probability as the first pitch sub-band. The length signal of the sub-band audio vector is calculated through the number of convolution layers preset in the LSTM network architecture:

[0067]

[0068] Wherein, E(n) is the energy feature of the sub-band audio vector, n is the current time point, x is the sub-band audio data, and L is the time window size for calculating energy.

[0069] It is worth mentioning that the features of the sub-band audio vector can be extracted through the LSTM network architecture. The computer autocorrelation coefficient method, cepstrum calculation, etc. can also be used to use energy as a feature and send it to the neural network to obtain the probability of each sub-band.

[0070] In one of the embodiments, the first pitch subband and the second pitch signal are input into a pitch detection model for model parameter training to obtain parameters of a neural network, and the pitch detection model is optimized according to the parameters to obtain a pre-trained pitch detection model.

[0071] In one embodiment, the pitch detection model is trained to calibrate the audio signal according to the cross entropy loss function and output a low-latency pitch signal:

[0072]

[0073] Among them, Loss is the cross entropy loss function, N is the filter order, y i is the first pitch subband, It is a low-latency pitch signal.

[0074] It should be understood that although Figure 1-2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0075] In one embodiment, Figure 4 As shown, a low-delay sub-band pitch detection device based on a neural network is provided, comprising: an audio signal acquisition module 402, a sub-band audio vector acquisition module 404, a first pitch sub-band acquisition module 406, a second pitch signal acquisition module 408, a model training module 410 and a low-delay pitch signal acquisition module 412, wherein:

[0076] The audio signal acquisition module 402 is used to acquire an audio signal.

[0077] The sub-band audio vector acquisition module 404 is used to construct a pitch detection model based on a neural network, input the audio signal as sample training data into the filter of the pitch detection model for filtering, and obtain multiple sub-band audio vectors.

[0078] The first pitch sub-band acquisition module 406 is used to extract features of each sub-band audio vector through a preset number of convolution layers in the neural network to obtain a first pitch sub-band.

[0079] The second pitch signal acquisition module 408 is used to obtain a second pitch signal by manually modifying the audio signal.

[0080] The model training module 410 is used to input the first pitch subband and the second pitch signal into the pitch detection model for model training to obtain a pre-trained pitch detection model.

[0081] The low-latency pitch signal acquisition module 412 is used to pre-train the pitch detection model to calibrate the audio signal according to the loss function and output a low-latency pitch signal.

[0082] In one of the embodiments, the first pitch subband acquisition module 406 is also used to extract features of each sub-band audio vector through a preset number of convolution layers in the LSTM network architecture, and use the softmax function to determine the probability of the zero-crossing rate of the sub-band audio vector using the extracted features, so as to obtain the sub-band audio vector corresponding to the maximum probability as the first pitch sub-band.

[0083] For the specific limitations of the low-delay band pitch detection device based on a neural network, please refer to the limitations of the low-delay band pitch detection method based on a neural network above, which will not be repeated here. Each module in the above-mentioned low-delay band pitch detection device based on a neural network can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0084] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a low-latency band pitch detection method based on a neural network is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0085] Those skilled in the art will understand that Figure 4-5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0086] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0087] Get the audio signal.

[0088] A pitch detection model based on a neural network is constructed, and the audio signal is input as sample training data into the filter of the pitch detection model for filtering to obtain multi-channel sub-band audio vectors.

[0089] Each sub-band audio vector is subjected to feature extraction through a preset number of convolutional layers in the neural network to obtain a first pitch sub-band.

[0090] A second pitch signal is obtained by manually modifying the audio signal.

[0091] The first pitch subband and the second pitch signal are input into a pitch detection model for model training to obtain a pre-trained pitch detection model.

[0092] The pre-trained pitch detection model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal.

[0093] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0094] Get the audio signal.

[0095] A pitch detection model based on a neural network is constructed, and the audio signal is input as sample training data into the filter of the pitch detection model for filtering to obtain multi-channel sub-band audio vectors.

[0096] Each sub-band audio vector is subjected to feature extraction through a preset number of convolutional layers in the neural network to obtain a first pitch sub-band.

[0097] A second pitch signal is obtained by manually modifying the audio signal.

[0098] The first pitch subband and the second pitch signal are input into a pitch detection model for model training to obtain a pre-trained pitch detection model.

[0099] The pre-trained pitch detection model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0101] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A low-delay band-splitting pitch detection method based on a neural network, characterized in that: The method comprises: Get audio signal; Constructing a pitch detection model based on a neural network, inputting the audio signal as sample training data into a filter of the pitch detection model for filtering, and obtaining a multi-channel sub-band audio vector; Each of the sub-band audio vectors is subjected to feature extraction through a preset number of convolutional layers in the neural network to obtain a first pitch sub-band; Obtaining a second pitch signal by manually modifying the audio signal; Inputting the first pitch subband and the second pitch signal into the pitch detection model for model training to obtain a pre-trained pitch detection model; The pre-trained pitch detection model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal.

2. The method according to claim 1, characterized in that Constructing a pitch detection model based on a neural network, inputting the audio signal as sample training data into a filter of the pitch detection model for filtering, and obtaining a multi-channel sub-band audio vector, including: A pitch detection model based on a neural network is constructed, the filter order is determined according to the sampling environment, the audio signal is input as sample training data into the filter corresponding to the filter order in the pitch detection model for order filtering, and a multi-channel sub-band audio vector is obtained.

3. The method according to claim 1, characterized in that Each of the sub-band audio vectors is subjected to feature extraction through a preset number of convolutional layers in the neural network to obtain a first pitch sub-band, including: Each sub-band audio vector is subjected to feature extraction through a preset number of convolutional layers in the LSTM network architecture, and the extracted features are subjected to a softmax function to determine the probability of the zero-crossing rate of the sub-band audio vector, so as to obtain the sub-band audio vector corresponding to the maximum probability value as the first pitch sub-band.

4. The method according to claim 3, characterized in that Inputting the first pitch subband and the second pitch signal into the pitch detection model for model training to obtain a pre-trained pitch detection model, including: The first pitch subband and the second pitch signal are input into the pitch detection model for model parameter training to obtain parameters of the neural network, and the pitch detection model is optimized according to the parameters to obtain a pre-trained pitch detection model.

5. The method according to any one of claims 1 to 4, characterized in that: The pre-trained pitch detection model proofreads the audio signal according to the loss function and outputs a low-latency pitch signal, including: The pitch detection model is trained, the audio signal is corrected according to the cross entropy loss function, and a low-latency pitch signal is output: Among them, Loss is the cross entropy loss function, N is the filter order, y i is the first pitch subband, It is a low-latency pitch signal.

6. A low-delay band-splitting pitch detection device based on a neural network, characterized in that: The device comprises: An audio signal acquisition module, used to acquire an audio signal; A sub-band audio vector acquisition module is used to construct a pitch detection model based on a neural network, input the audio signal as sample training data into the filter of the pitch detection model for filtering, and obtain multiple sub-band audio vectors; A first pitch sub-band acquisition module, configured to extract features of each sub-band audio vector through a preset number of convolution layers in the neural network to obtain a first pitch sub-band; A second pitch signal acquisition module, used to obtain a second pitch signal by manually correcting the audio signal; A model training module, used for inputting the first pitch subband and the second pitch signal into the pitch detection model for model training to obtain a pre-trained pitch detection model; A low-delay pitch signal acquisition module is used for the pre-trained pitch detection model to calibrate the audio signal according to the loss function and output a low-delay pitch signal.

7. The device according to claim 6, characterized in that The first pitch subband acquisition module is also used to extract features of each subband audio vector through a preset number of convolution layers in the LSTM network architecture, and use the softmax function to determine the probability of the zero-crossing rate of the subband audio vector using the extracted features, so as to obtain the subband audio vector corresponding to the maximum probability value as the first pitch subband.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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

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