Subspace projection cepstral coefficients for noise robust acoustic event recognition

Sangwook Park; Younglo Lee; David K. Han; Hanseok Ko

doi:10.1109/ICASSP.2017.7952258

Subspace projection cepstral coefficients for noise robust acoustic event recognition

Sangwook Park, Younglo Lee, David K. Han, Hanseok Ko

School of Electrical Engineering

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

4 Citations (Scopus)

Abstract

In this paper, a novel feature for noise robust sound event recognition is proposed. The proposed feature is obtained by a two-step procedure. First, a subspace bank is established via target event analysis in complex vector space. Then, by projecting observation vectors onto the subspace bank, noise effect can be reduced while generating discriminant characters originated from differing event subspaces. To demonstrate robustness of the proposed feature, experiments with several classifiers were conducted with varying SNR cases under four noisy environments. According to the experimental results, the proposed method has shown superior performance over prominent conventional methods.

Original language	English
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	761-765
Number of pages	5
ISBN (Electronic)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7952258
Publication status	Published - 2017 Jun 16
Event	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: 2017 Mar 5 → 2017 Mar 9

Other

Other	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/Territory	United States
City	New Orleans
Period	17/3/5 → 17/3/9

Keywords

acoustic event classification
principal component analysis
robust feature extraction
subspace learning

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP.2017.7952258

Cite this

Park, S., Lee, Y., Han, D. K., & Ko, H. (2017). Subspace projection cepstral coefficients for noise robust acoustic event recognition. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 761-765). Article 7952258 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952258

Subspace projection cepstral coefficients for noise robust acoustic event recognition. / Park, Sangwook; Lee, Younglo; Han, David K. et al.
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 761-765 7952258.

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

Park, S, Lee, Y, Han, DK & Ko, H 2017, Subspace projection cepstral coefficients for noise robust acoustic event recognition. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7952258, Institute of Electrical and Electronics Engineers Inc., pp. 761-765, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, New Orleans, United States, 17/3/5. https://doi.org/10.1109/ICASSP.2017.7952258

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abstract = "In this paper, a novel feature for noise robust sound event recognition is proposed. The proposed feature is obtained by a two-step procedure. First, a subspace bank is established via target event analysis in complex vector space. Then, by projecting observation vectors onto the subspace bank, noise effect can be reduced while generating discriminant characters originated from differing event subspaces. To demonstrate robustness of the proposed feature, experiments with several classifiers were conducted with varying SNR cases under four noisy environments. According to the experimental results, the proposed method has shown superior performance over prominent conventional methods.",

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AB - In this paper, a novel feature for noise robust sound event recognition is proposed. The proposed feature is obtained by a two-step procedure. First, a subspace bank is established via target event analysis in complex vector space. Then, by projecting observation vectors onto the subspace bank, noise effect can be reduced while generating discriminant characters originated from differing event subspaces. To demonstrate robustness of the proposed feature, experiments with several classifiers were conducted with varying SNR cases under four noisy environments. According to the experimental results, the proposed method has shown superior performance over prominent conventional methods.

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Subspace projection cepstral coefficients for noise robust acoustic event recognition

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Keywords

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