Audio classification method based on convolution neural network and random forest

Abstract

The invention discloses an audio classification method based on a convolution neural network and a random forest. The method comprises the following steps: S1, carrying out spectral analysis includingsegmenting, framing, windowing and Fourier transform on an original audio data set to obtain a frequency spectrogram corresponding to an original audio file; S2, training a convolution neural networkfeature extractor by taking the obtained frequency spectrogram as an input; S3, removing a softmax layer of the convolution neural network and extracting high-level features of the frequency spectrogram; S4, training a random forest classifier by utilizing the extracted high-level features of the frequency spectrogram; S5, based on the extracted high-level features of the convolution neural network, classifying audios by utilizing the trained random forest. According to the audio classification method disclosed by the invention, feature extraction is performed based on the convolution neuralnetwork, so that the tedious process of manual construction of extraction features is avoided; meanwhile, for solving the problem of insufficient generalization ability caused by using the softmax asthe convolution neural network classifier, the softmax layer of the convolution neural network is replaced with the random forest which is used as a final classifier, so that higher accuracy and recall rate are realized in the testing process.

Description

technical field [0001] The invention belongs to the field of machine learning and relates to an audio classification method based on a convolutional neural network and a random forest. Background technique [0002] The development of the Internet and multimedia technology has filled our lives with a large amount of audio, especially various music websites, which have a large number of audio files with different styles. In the face of massive audio, audio retrieval can help us quickly and accurately find the audio files we need. Audio classification is the premise of audio retrieval, but manual classification of a large number of audio files is a very time-consuming and tedious task. With people's hearing fatigue, the accuracy of manual classification will also decrease. For a large number of audio files, fast and accurate automatic classification is very necessary. There are many studies on audio classification methods. For example, a two-level audio classification method...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an audio classification method based on convolutional neural network and random forest, which uses convolutional neural network to automatically extract high-level features, and uses random forest to solve the problem that the generalization ability of a single classifier is not strong , with high precision and recall

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