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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.
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.
Keywords
Speech/Music Classification; Enhanced Voice Service, Long Short-Term Memory, Big Data
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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