Haar-like filtering based speech detection using integral signal for sensornet

Jun Nishimura, Tadahiro Kuroda

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Speech detection using haar-like filtering is proposed as a new and very low calculation cost method for sensornet applications. The simple haar-like filters having variable filter width and shift width are trained to learn appropriate filter parameters from the training samples to detect speech. To further decrease the calculation cost, the use of intermediate signal representation called "integral signal" is proposed. Our method yielded speech/nonspeech classification accuracy of 97.44% for the input length of 0.1s. Compared with high performance feature extraction method MFCC (Mel-Frequency Cepstrum Coefficient), the proposed haar-like filtering can be approximately 93.71% efficient in terms of the total amount of add and multiply calculations while capable of achieving the error rate of only 2.56% relative to MFCC.

本文言語English
ホスト出版物のタイトルProceedings of the 3rd International Conference on Sensing Technology, ICST 2008
ページ52-56
ページ数5
DOI
出版ステータスPublished - 2008
外部発表はい
イベント3rd International Conference on Sensing Technology, ICST 2008 - Tainan, Taiwan, Province of China
継続期間: 2008 11月 302008 12月 3

出版物シリーズ

名前Proceedings of the 3rd International Conference on Sensing Technology, ICST 2008

Other

Other3rd International Conference on Sensing Technology, ICST 2008
国/地域Taiwan, Province of China
CityTainan
Period08/11/3008/12/3

ASJC Scopus subject areas

  • ソフトウェア
  • 電子工学および電気工学

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