Detection of Sound Event under Low SNR Using Multi-band Power Distribution

Ying LI; Lingfei WU

doi:10.11999/JEIT180180

Volume 40 Issue 12

Nov. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2905-2912

Ying LI, Lingfei WU. Detection of Sound Event under Low SNR Using Multi-band Power Distribution[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2905-2912. doi: 10.11999/JEIT180180

Citation:

Ying LI, Lingfei WU. Detection of Sound Event under Low SNR Using Multi-band Power Distribution[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2905-2912. doi: 10.11999/JEIT180180

Ying LI, Lingfei WU. Detection of Sound Event under Low SNR Using Multi-band Power Distribution[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2905-2912. doi: 10.11999/JEIT180180

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Detection of Sound Event under Low SNR Using Multi-band Power Distribution

doi: 10.11999/JEIT180180

Ying LI^,,
Lingfei WU

1.
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
2.
Fujian Province Key Laboratory of Information Security of Network Systems, Fuzhou University, Fuzhou 350116, China

Funds: The National Natural Science Foundation of China (61075022), The Natural Science Foundation of Fujian Province (2018J01793)

Received Date: 2018-02-09
Rev Recd Date: 2018-07-09

Available Online: 2018-07-26

Publish Date: 2018-12-01

Abstract

Abstract

As to the problem of sound event detection in low Signal-Noise-Ratio (SNR) noise environments, a method is proposed based on discrete cosine transform coefficients extracted from multi-band power distribution image. First, by using gammatone spectrogram analysis, sound signal is transformed into multi-band power distribution image. Next, 8×8 size blocking and discrete cosine transform are applied to analyze the multi-band power distribution image. Based on the main Zigzag coefficients which are scanned from the discrete cosine transform coefficients, features of sound event are constructed. Finally, features are modeled and detected through random forests classifier. The results show that the proposed method achieves a better detection performance in low SNR comparing to other methods.
- Sound event detection,
- Multi-band power distribution,
- Random forests,
- Discrete cosine transform

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References(26)

References

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