Parallel Algorithm Design for Audio Feature Extraction

DOI

10.2991/icmmct-17.2017.229How to use a DOI?

Keywords

Feature Extraction; Sound Recognition; Parallel Algorithm

Abstract

Audio feature extraction is a very important technique in the field of sound processing. It extremely impacts the effectiveness and correctness of sound recognition, sound verification, etc. It is a computation intensive stage in the whole sound recognition process, which is a challenging for acceleration. In this paper, a coarse-grained parallel feature extraction algorithm for high throughput of audio slices is proposed to improve the efficiency of audio feature extraction. Three typical audio feature extraction algorithms, Mel Frequency Cepstrum Coefficients(MFCC), Spectrogram image features(SIF), Octave-Based Spectral Contrast, are chosen to parallelize. Experiments results on different platforms show that the speedup of accelerated audio feature extraction is up to 17.23 on the platform with 16 cores 32 threads.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - XiaoCheng Luo
AU  - Jingfei Jiang
AU  - Junjie Zhu
AU  - Yong Dou
PY  - 2017/04
DA  - 2017/04
TI  - Parallel Algorithm Design for Audio Feature Extraction
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 1163
EP  - 1168
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.229
DO  - 10.2991/icmmct-17.2017.229
ID  - Luo2017/04
ER  -