Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients

Daniel M. Rasetshwane; J. Robert Boston; Ching Chung Li; John D. Durrant

doi:10.1109/ASPAA.2007.4393030

Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients

Daniel M. Rasetshwane, J. Robert Boston, Ching Chung Li, John D. Durrant

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Speech transients have been shown to be important cues for identifying speech sounds, and amplification of transients can improve the intelligibility of speech in noise. We have developed a time-frequency approach to identify transients that uses a pre-processing filter, but optimal filter parameters are difficult to determine due to the large number of possibilities. This paper describes the use of the Articulation Index (AI) to evaluate the effects of different high-pass filters on our algorithm. The best filter was found to depend on signal-to-noise ratio (SNR). AI results should be interpreted with caution, but they appear to provide a reasonable approach to selecting a pre-processing filter.

Original language	English (US)
Title of host publication	2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA
Pages	179-182
Number of pages	4
DOIs	https://doi.org/10.1109/ASPAA.2007.4393030
State	Published - 2007
Externally published	Yes
Event	2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA - New Paltz, NY, United States Duration: Oct 21 2007 → Oct 24 2007

Publication series

Name	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Other

Other	2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA
Country/Territory	United States
City	New Paltz, NY
Period	10/21/07 → 10/24/07

ASJC Scopus subject areas

Signal Processing

Access to Document

10.1109/ASPAA.2007.4393030

Cite this

Rasetshwane, D. M., Boston, J. R., Li, C. C., & Durrant, J. D. (2007). Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients. In 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA (pp. 179-182). Article 4393030 (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics). https://doi.org/10.1109/ASPAA.2007.4393030

Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients. / Rasetshwane, Daniel M.; Boston, J. Robert; Li, Ching Chung et al.
2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA. 2007. p. 179-182 4393030 (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rasetshwane, DM, Boston, JR, Li, CC & Durrant, JD 2007, Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients. in 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA., 4393030, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 179-182, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA, New Paltz, NY, United States, 10/21/07. https://doi.org/10.1109/ASPAA.2007.4393030

Rasetshwane DM, Boston JR, Li CC, Durrant JD. Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients. In 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA. 2007. p. 179-182. 4393030. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics). doi: 10.1109/ASPAA.2007.4393030

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AB - Speech transients have been shown to be important cues for identifying speech sounds, and amplification of transients can improve the intelligibility of speech in noise. We have developed a time-frequency approach to identify transients that uses a pre-processing filter, but optimal filter parameters are difficult to determine due to the large number of possibilities. This paper describes the use of the Articulation Index (AI) to evaluate the effects of different high-pass filters on our algorithm. The best filter was found to depend on signal-to-noise ratio (SNR). AI results should be interpreted with caution, but they appear to provide a reasonable approach to selecting a pre-processing filter.

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Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients

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