Preprint Article Version 1 This version is not peer-reviewed

Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM

Version 1 : Received: 5 November 2018 / Approved: 5 November 2018 / Online: 5 November 2018 (17:02:36 CET)

A peer-reviewed article of this Preprint also exists.

Kang, S.-I.; Lee, S. Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM. Symmetry 2018, 10, 605. Kang, S.-I.; Lee, S. Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM. Symmetry 2018, 10, 605.

Journal reference: Symmetry 2018, 10, 605
DOI: 10.3390/sym10110605

Abstract

Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, a new technique to improve the robustness of speech/music classifier for 3GPP enhanced voice service (EVS) using long short-term memory (LSTM) is proposed. For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. Experiments show that LSTM-based speech/music classification produces better results than conventional EVS under a variety of conditions and types of speech/music data.

Subject Areas

Speech/Music Classification; Enhanced Voice Service, Long Short-Term Memory, Big Data

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