Acoustic scene clustering method for joint optimization of deep transformation features and clustering process

Abstract

The invention discloses an acoustic scene clustering method for joint optimization of deep transformation features and the clustering process. The acoustic scene clustering method comprises the following steps that a, logarithmic Mehr spectrum features of all samples are extracted and serve as an initial class, and a convolutional neural network is initialized; b, the logarithmic Mehr spectrum features of the samples are input into the convolutional neural network, and the deep transformation features are extracted; c, two most similar classes are combined through a agglomerative and hierarchical clustering algorithm, new class tags and samples are obtained and used for updating the convolutional neural network, one is subtracted from the class number, and the logarithmic Mehr spectrum features of the samples are transformed into the deep transformation features through the updated convolutional neural network; d, if the current class number is equal to the true class number, clustering is stopped, the jointly optimized acoustic scene clustering result and the convolutional neural network are obtained, otherwise, the step c is carried out. According to the method, deep transformation feature extracting and clustering are alternately carried out, the joint optimization result is obtained, and compared with a traditional clustering method, the performance is better. Compared witha traditional classification method, the method has higher universality.

Description

technical field [0001] The invention relates to the technical field of audio signal processing and pattern recognition, in particular to an acoustic scene clustering method for joint optimization of deep transformation features and clustering process. Background technique [0002] Acoustic scene clustering (ASC) is to compare the similarity of collected audio samples of various acoustic scenes, and merge the audio samples of the same category together. The purpose of acoustic scene clustering is mainly to make the machine more intelligent, so that it has the ability to distinguish the surrounding acoustic environment similar to humans, so as to provide more intelligent services for humans. Acoustic scene clustering technology is an important basis for audio monitoring, automatic assisted driving, multimedia content analysis and retrieval and other application fields, and has important research value and practical significance. [0003] The traditional supervised acoustic sc...

AI Technical Summary

Problems solved by technology

The current unsupervised sound scene classification method generally separates audio feature extraction and sound scene clustering, and cannot obtain optimal sound scene clustering results.

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