An Audio Data Retrieval Method Based on Similarity Matrix Fusion

A similarity matrix, audio data technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of difficult to further improve the retrieval accuracy.

Inactive Publication Date: 2016-08-10
NORTHWESTERN POLYTECHNICAL UNIV
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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

Method used

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  • An Audio Data Retrieval Method Based on Similarity Matrix Fusion
  • An Audio Data Retrieval Method Based on Similarity Matrix Fusion
  • An Audio Data Retrieval Method Based on Similarity Matrix Fusion

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Embodiment Construction

[0022] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0023] The hardware environment used for implementation is: AMD Athlon 64×2 5000+ computer, 2GB memory, 256M graphics card, and the running software environment is: Matlab2009a and Windows XP. We have realized the method that the present invention proposes with Matlab software.

[0024] The present invention is specifically implemented as follows:

[0025] The flow chart of the present invention is attached figure 1 shown. The 264 audio data used for retrieval include three categories: 100 classical audio data, 100 popular audio data and 64 speech audio data. The two features are high-level features and low-level features, and the specific steps are as follows:

[0026] 1. Calculate two kinds of features X of N=264 audio data 1 ,X 2 ,...,X N and Y 1 ,Y 2 ,...,Y N The Laplacian matrix L 1 and L 2 , X 1 ,X 2 ,...,X N Represents the high-level feature...

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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 ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 韩军伟吉祥郭雷胡新韬
Owner NORTHWESTERN POLYTECHNICAL UNIV
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