Sound source separation algorithm of end-to-end time domain multi-scale convolutional neural network

A convolutional neural network and multi-scale technology, applied in the field of sound source separation algorithms, can solve problems such as limiting applicability and increasing the minimum delay of the model, and achieve the goals of improving separation accuracy, processing speed, strong generalization ability and accuracy Effect

Pending Publication Date: 2021-08-27
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

This limits its applicability to low-latency, real-time applications, increasing the minimum latency of the model

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  • Sound source separation algorithm of end-to-end time domain multi-scale convolutional neural network
  • Sound source separation algorithm of end-to-end time domain multi-scale convolutional neural network
  • Sound source separation algorithm of end-to-end time domain multi-scale convolutional neural network

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Embodiment Construction

[0026] The end-to-end time-domain multi-scale convolutional neural network sound source separation algorithm method, the model is built with the technical background of convolutional neural network, gated linear unit, causal dilated convolution, residual module and depth separable convolutional network, the specific The flow chart of the sound source separation technology solution is shown in the accompanying drawings figure 1 .

[0027] The present invention will choose two kinds of musical instrument audio frequency and human voice audio data set as experimental object, select open source piano audio frequency (MAPS) data set, violin data set (Bash10), and human voice audio data set (MIR-1K) because the model has relatively Strong generalization ability, not only for the separation of sound sources of a specified musical instrument, so the data set can be replaced with the target data set that needs to be separated. The selected Piano Audio (MAPS) dataset contains piano aud...

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Abstract

The invention discloses a sound source separation algorithm of a multi-scale convolutional neural network with a real-time processing function. The sound source separation algorithm mainly comprises the steps of making a mixed data set; carrying out feature extraction on the mixed audio through a designed multi-scale encoder so that the separation performance is improved; carrying out modeling on the extracted features through multiple groups of improved TCNs, and replacing standard convolution in a convolution block with depth separable convolution (S_conv); introducing a gating mechanism into each TCN block, and processing a feature information stream by using a full convolution gated linear unit (GLU); and finally, restoring a pure audio waveform through a multi-scale decoder. According to the multi-scale-based time domain audio operation method, a TCN network is used for building a sound source separation model, the defect that phase information is ignored by using short-time Fourier transform traditionally is overcome, a lightweight network is built, information flow is effectively controlled, and real-time and accurate separation is achieved.

Description

technical field [0001] The invention belongs to the audio signal processing technology, and relates to a multi-scale sound source separation algorithm with a convolutional neural network with a real-time function and a gating mechanism, with the purpose of separating human voice from musical instrument performance. Background technique [0002] As an art, music has developed rapidly from ancient times to the present, and many outstanding musicians have emerged, producing many popular works of art. Traditional music is handed down in the form of scores and performances. The audio played by multiple instruments makes the mixed instrument audio emerge as the times require. Mixed audio is not just a variety of musical instruments. Music works are more mixed voice audio mixed with singing voice and musical instrument audio. The research on voice and musical instrument audio in the traditional field is only based on their different acoustic principles. [0003] With the developm...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L21/0272G10L25/30
CPCG10L21/0272G10L25/30
Inventor 卢迪邢湘琦
Owner HARBIN UNIV OF SCI & TECH
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