A Multi-Resolution Approach for Audio Classification

Sergey Voronin; Alexander Grushin

doi:10.20944/preprints201804.0258.v2

Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 18 April 2018 / Approved: 20 April 2018 / Online: 20 April 2018 (04:30:33 CEST)
Version 2 : Received: 19 July 2018 / Approved: 19 July 2018 / Online: 19 July 2018 (05:53:20 CEST)

We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.

audio classification; multi-resolution analysis; LSTM; auto-ml

Computer Science and Mathematics, Information Systems

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A Multi-Resolution Approach for Audio Classification

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