Preprint Article Version 1 This version is not peer-reviewed

A Multi-Resolution Approach for Audio Classification

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)

How to cite: Voronin, S.; Grushin, A. A Multi-Resolution Approach for Audio Classification. Preprints 2018, 2018040258 (doi: 10.20944/preprints201804.0258.v1). Voronin, S.; Grushin, A. A Multi-Resolution Approach for Audio Classification. Preprints 2018, 2018040258 (doi: 10.20944/preprints201804.0258.v1).

Abstract

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. A conditional probability approach is outlined for combining the predictions of different classifiers, trained over distinct scale feature sets. Initial results show an improvement in multi-class classification accuracy.

Subject Areas

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

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