An Audio Data Retrieval Method Based on Similarity Matrix Fusion

Abstract

The invention relates to a method for retrieving audio data on the basis of similarity matrix fusion. The method is characterized by comprising firstly, computing Laplacian matrixes of features of different types of audio data; secondly, computing feature values and feature vectors of the Laplacian matrixes of the different types of audio data and respectively finding out the feature vectors corresponding to the front M maximum feature values in the Laplacian matrixes; thirdly, respectively computing similarity matrixes of the feature vectors of the different types of audio data, multiplying corresponding elements of the similarity matrixes of the feature vectors to obtain fused similarity matrixes; fourthly, acquiring a score of each audio data by the aid of the fused similarity matrixes for each inquired target audio data, sorting the audio data according to the scores of the audio data from high to low, counting the quantity of the audio data which are of the type the same with the target audio data in the front multiple sorted audio data and computing the retrieval accuracy. The method has the advantages that the features of the various audio data can be retrieved in a fused manner by the method, and the fused retrieval accuracy is greatly improved as compared with the retrieval accuracy obtained before the similarity matrixes are fused.

Description

technical field [0001] The invention relates to an audio data retrieval method based on similarity matrix fusion, which can be applied to the retrieval of different types of audio data. Background technique [0002] With the development of Internet technology and audio technology, the amount of audio data has increased geometrically. How to retrieve the audio data that users need from the massive audio data has become a hot and difficult issue in multimedia technology. Scholars at home and abroad have proposed a variety of features to represent audio data, making audio data retrieval more accurate. In addition, many methods for audio data retrieval have been proposed, which can also improve the accuracy of audio data retrieval. However, most of these methods are based on a single feature for retrieval, and these features are often based on traditional features such as musical scales, so it is difficult to further improve the retrieval accuracy. Contents of the invention ...

AI Technical Summary

Problems solved by technology

However, most of these methods are based on a single feature for retrieval, and these features are often based on traditional features such as scales, and it is difficult to further improve the retrieval accuracy

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