Low cost speech detection using Haar-like filtering for sensornet

Jun Nishimura, Tadahiro Kuroda

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    13 Citations (Scopus)

    Abstract

    Haar-like filtering based speech detection is proposed as a new and very low calculation cost method for sensornet applications. The simple haarlike filters having variable filter width and shift width are trained to learn appropriate filter parameters from the training samples to detect speech. Our method yielded speech/nonspeech classification accuracy of 96.93% 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 85.77% efficient in terms of the amount of add and multiply calculations while capable of achieving the error rate of only 3.03% relative to MFCC.

    Original languageEnglish
    Title of host publication2008 9th International Conference on Signal Processing, ICSP 2008
    Pages2608-2611
    Number of pages4
    DOIs
    Publication statusPublished - 2008 Dec 1
    Event2008 9th International Conference on Signal Processing, ICSP 2008 - Beijing, China
    Duration: 2008 Oct 262008 Oct 29

    Publication series

    NameInternational Conference on Signal Processing Proceedings, ICSP

    Other

    Other2008 9th International Conference on Signal Processing, ICSP 2008
    CountryChina
    CityBeijing
    Period08/10/2608/10/29

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Computer Science Applications

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  • Cite this

    Nishimura, J., & Kuroda, T. (2008). Low cost speech detection using Haar-like filtering for sensornet. In 2008 9th International Conference on Signal Processing, ICSP 2008 (pp. 2608-2611). [4697683] (International Conference on Signal Processing Proceedings, ICSP). https://doi.org/10.1109/ICOSP.2008.4697683