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A music automatic labeling method based on label depth analysis

An automatic labeling and music technology, applied in the field of music information research, can solve the problems of poor learning effect of deep neural network, and achieve the effect of improving performance and improving robustness

Inactive Publication Date: 2019-06-21
SOUTH CHINA UNIV OF TECH
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

These problems are an important reason for the label noise in the dataset, and the label noise in the dataset will lead to poor learning effect of the deep neural network.

Method used

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  • A music automatic labeling method based on label depth analysis
  • A music automatic labeling method based on label depth analysis
  • A music automatic labeling method based on label depth analysis

Examples

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Embodiment

[0048] This embodiment provides a music automatic labeling method based on tag depth analysis, the flow chart is as follows figure 1 shown, including the following steps:

[0049] S1. Collect music data and perform data cleaning in combination with the music label system;

[0050] S2. Sampling the music data, converting it into a mel spectrogram and performing data slicing;

[0051] S3. Construct an audio multi-level feature extraction network based on a one-dimensional convolutional network, and perform parameter pre-training through supervised learning;

[0052] S4. Carry out music label vector representation learning based on a two-dimensional convolutional network, and obtain music label features;

[0053] S5. Realize the feature aggregation of audio multi-level features and music label features;

[0054] S6. Perform final music label prediction based on the aggregated features.

[0055] The above method uses a one-dimensional convolutional network to construct an audi...

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Abstract

The invention discloses a music automatic labeling method based on label depth analysis. The method comprises the following steps: S1, collecting music data and cleaning the data by combining a musiclabel system; S2, sampling the music data, converting the music data into a Mel-frequency spectrogram, and slicing the Mel-frequency spectrogram; S3, constructing an audio multi-level feature extraction network based on the one-dimensional convolutional network, and performing parameter pre-training through supervised learning; S4, performing music label vector representation learning based on thetwo-dimensional convolutional network, and obtaining music label characteristics; S5, realizing feature aggregation of the audio multi-level features and the music tag features; and S6, performing final music label prediction based on the aggregation characteristics. According to the method, the difficulty that a traditional music labeling mode cannot be applied to a large-scale music data set isovercome, the music is automatically labeled according to the audio content, the workload of manually maintaining a music label library is reduced, and the method has very good usability.

Description

technical field [0001] The invention relates to the field of music information research, in particular to an automatic music tagging method based on tag depth analysis. Background technique [0002] In recent years, digital music has become increasingly popular, and the number of music that users can access on the Internet has exceeded 30 million. In addition, as users often post a large number of original songs, cover songs and other multimedia resources on social media, the types of music are increasingly diversified. As an effective means of organizing massive music data, rich music annotation information is of great value for online music products. Music annotation information also provides high potential economic value for music retrieval and music discovery services. Based on this, music automatic labeling, as an effective method for enriching music information, has received more and more attention and research. [0003] Deep neural networks have been successfully a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/68G06F16/65G06N3/04G06N3/08
Inventor 王振宇萧永乐张睿雷昶高雨轩
Owner SOUTH CHINA UNIV OF TECH
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