Cosine similarity based dictionary learning and source recovery for classification of diverse audio sources

Girish, KV Vijay and Ananthapadmanabha, TV and Ramakrishnan, AG (2017) Cosine similarity based dictionary learning and source recovery for classification of diverse audio sources. In: 2016 IEEE Annual India Conference, INDICON 2016, 16-18 December 2016, Bangalore, India, pp. 1-6.

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Abstract

A dictionary learning based audio source classification algorithm is proposed. Cosine similarity measure is used to select the atoms during dictionary learning. Three proposed objective measures, namely, signal to distortion ratio (SDR), the number of non-zero weights and the sum of weights have been used for classification. A frame-wise source classification accuracy of 98.86% is obtained for twelve different sources using SDR measure and a secondary support vector machine classifier. 100% accuracy has been obtained using moving SDR accumulated over 14 successive frames. For ten of the audio sources tested, 100% accuracy requires accumulation of only 6 frames of a signal.

Item Type:	Conference Paper
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The Copyright of this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	audio classification; cosine similarity; Dictionary learning; source recovery; sparse representation; Audio classification; Cosine similarity; Cosine similarity measures; Dictionary learning; Secondary supports; Signal-to-distortion ratios; Source classification; Sparse representation; Audio acoustics
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	11 Jun 2022 09:12
Last Modified:	11 Jun 2022 09:12
URI:	https://eprints.iisc.ac.in/id/eprint/73265

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