Music audio classification method based on convolutional recurrent neural network

A technology of recurrent neural network and classification method, applied in the field of music classification, can solve the problem that the characteristics of classification tasks are not universal

Pending Publication Date: 2021-01-08
SOUTH CHINA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0006] To sum up, the problem with the current method is that the design of manual features requires background knowledge in the music field, and the features of diff

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  • Music audio classification method based on convolutional recurrent neural network
  • Music audio classification method based on convolutional recurrent neural network
  • Music audio classification method based on convolutional recurrent neural network

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Embodiment Construction

[0067] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments, but the embodiments of the present invention are not limited thereto.

[0068] Such as figure 1 As shown, the present embodiment provides a music audio classification method based on a convolutional cyclic neural network, comprising the following steps:

[0069] S1. Label the audio of the music to obtain a music label dataset with music labels. The size of the dataset marked here is 1000; there are two kinds of music annotation datasets used, namely GTZAN dataset and MagnaTagATune dataset; the GTZAN dataset is divided into training set, verification set and The test set, and the MagnaTagTune data set contains 16 subdirectories from 0 to f, the data in the 0 to b directory is used as the training set, the data set in the c directory is used as the verification set, and the remaining data in the d to f directory data as a test s...

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Abstract

The invention discloses a music audio classification method based on a convolutional recurrent neural network. The method comprises the following steps: S1, annotating music audios to obtain a music annotation data set; S2, enhancing the training data of the data set by adopting a music data enhancement method; S3, framing and windowing the audio signals of the music in the data set, and obtaininga Mel sound spectrum corresponding to the audio through short-time Fourier transform and Mel scale transform; S4, constructing a music audio classification model based on a convolutional recurrent neural network; S5, inputting the Mel sound spectrum of the training data into the music audio classification model based on a convolutional recurrent neural network for iterative training; and S6, inputting a Mel sound spectrum corresponding to the music, and predicting the label of the music. The method provided by the invention can improve the ability of the network to extract the sound spectrumfeatures, and obtain better music overall feature representation, thereby improving the accuracy of music audio classification.

Description

technical field [0001] The present invention relates to the field of music classification, more specifically, to a music audio classification method based on a convolutional cyclic neural network. Background technique [0002] With the rapid development of multimedia and digital technology, there are more and more digital music resources on the Internet, and consumers' music consumption habits have shifted from physical music to online music platforms. Massive music resources and a huge online music library inspire users to generate various complex music retrieval needs. For example, at a certain moment, users are eager to listen to songs of a certain genre or with a certain emotion. Critical to the quality of music retrieval. In addition to music retrieval, many recommendation and subscription scenarios also require song category information to provide users with more accurate content. [0003] At present, the labeling of music categories is mainly through manual and soci...

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Application Information

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IPC IPC(8): G06F16/65G06N3/04
CPCG06F16/65G06N3/044G06N3/045
Inventor 王振宇高雨轩
Owner SOUTH CHINA UNIV OF TECH
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