Complex audio segmentation clustering method based on bottleneck feature

Abstract

The invention discloses a complex audio segmentation clustering method based on a bottleneck feature. The method comprises the steps that a deep neural network with a bottleneck layer is constructed; a complex audio stream is read, and endpoint detection is carried out on the complex audio stream; the audio feature of a non-silent segment is extracted and input into the deep neural network; the bottleneck feature is extracted from the bottleneck layer of the deep neural network; the bottleneck feature is used as input, and an audio segmentation method based on the Bayesian information criterion is used, so that each audio segment contains only one kind of audio type and adjacent audio segments have different audio types; a spectral clustering algorithm is used to cluster segmented audio segments to acquire the number of audio types of complex audios; and the audio segments of the same audio type are merged together. According to the invention, the used bottleneck feature is a deep transform feature, can more effectively describe the feature difference of the complex audio type than a traditional audio feature, and acquires an excellent effect in complex audio segmentation clustering.

Description

technical field [0001] The invention relates to audio signal processing and pattern recognition technology, in particular to a complex audio segmentation and clustering method based on bottleneck features. Background technique [0002] With the development and popularization of multimedia acquisition equipment, the Internet and cloud storage platforms, the demand for analysis and retrieval of massive and complex audio content is becoming increasingly urgent. As an unsupervised method, complex audio segmentation and clustering are one of the important means of audio content analysis. Although manual labeling can be used to find out the audio types in the audio stream, the cost of manual labeling is high, subjectivity is strong, and the efficiency is low, while the supervised audio classification method needs to know the audio types in the audio stream in advance, and train specific audio types in advance. type of classifier. Therefore, unsupervised complex audio segmentatio...

AI Technical Summary

Problems solved by technology

Although manual labeling can be used to find out the audio types in the audio stream, the cost of manual labeling is high, subjectivity is strong, and the efficiency is low, while the supervised audio classification method needs to know the audio types in the audio stream in advance, and train specific audio types in advance. type classifier

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