Supervised dictionary learning audio classification method and system based on data driving, and medium

Abstract

The invention discloses a supervised dictionary learning audio classification method and system based on data driving and a medium. The method comprises the steps of determining a sample set category number; training a specific class dictionary by using the input samples and the corresponding class labels; obtaining a sparse code of the input sample by using the trained dictionary, and training an SVM classifier by taking the sparse code as a feature; and classifying the input samples by using the trained dictionary and the trained SVM classifier, and outputting a prediction label. The intra-class uniformity is minimized and the class separability is maximized by learning one dictionary through each class, the sparsity is improved so as to control the complexity of decomposition of signals on the dictionary, meanwhile, class-based reconstruction errors are minimized, and the pairwise orthogonality of the dictionary is improved. The invention can be widely applied to a plurality of scenes, such as computational auditory scene recognition and music chord recognition; and the test on the data set is relatively stable, and the generalization ability is excellent.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for efficient training dictionaries that are more accurate than existing methods like histogram or spectral analysis techniques. It also has improved accuracy when applied across different scenarios where sound signals may vary greatly due to factors like background noise levels. Overall, this new technique helps create better quality speech recognizers with high confidence scores at various stages during security testing processes.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving dictionaries trained with data from an unstructured dataset such as speech or image signals without losing important parts like sound quality due to noise interference during transmission over communication networks.

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