Music genre classification method and system based on a feature weighted fuzzy support vector machine

Abstract

The invention relates to a music genre classification method and system based on a feature weighted fuzzy support vector machine, belonging to the technical field of music content retrieval and pattern recognition. The classifier of the invention selects the fuzzy support vector machine, and can assign the corresponding membership degree according to the difference of the contribution of differentinput samples to the classification, so as to reduce the influence of the noise. The feature weights calculated by reliefF feature selection algorithm are used to determine the membership degree of fuzzy support vector machine, which takes into account the different influence of features with different weights on classification. Aiming at the inseparable point of blind area, the weighted Euclidean distance from the point to the center of each class is used to divide the multi-class probability, which accords with the essence of music diversification.

Description

technical field [0001] The invention relates to a music genre classification method and system based on a feature weighted fuzzy support vector machine, and belongs to the technical field of music content retrieval and pattern recognition. Background technique [0002] Music is an art that people use to express their life and express their emotions. Music genres are classification labels created by humans, and experts organize music by certain similarities. With the continuous increase of music data, the increasingly large digital music database requires intelligent and automatic classification management, and the classification of music genres has attracted more and more attention from the society and academic circles. However, the current trend of music development is becoming more and more diversified, and a piece of music may be integrated into multiple genres. [0003] The music genre classification system has three components: music feature extraction and selection; ...

AI Technical Summary

Problems solved by technology

However, the current research work rarely takes into account the ambiguity of music genres, and finally only outputs the most likely genre, which leads to incomplete music genre information and cannot keep up with the more diversified development trend of music today.

In addition, the problem that the SVM classifier is greatly affected by noise has not been well resolved in the classification of music genres.

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