NDLI: Lower SNR sound event recognition using noisy training sample

Content Provider	IEEE Xplore Digital Library
Author	Lin, Wei Li, Ying
Copyright Year	2015
Description	Author affiliation: College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China (Lin, Wei; Li, Ying)
Abstract	Sound event recognition becomes a basic task in some applications. However, in low SNR condition, the accuracy rate is easily affected by the acoustic scene. To address the problem, this paper proposes a framework consisting of empirical mode decomposition (EMD), gray level co-occurrence matrix combined with higher-order singular value decomposition (GLCM-HOSVD), and random forests (RF). We use a voting method based on the first to sixth intrinsic mode functions (IMFs) which is generated from EMD, to detect the endpoint of sound events and estimate the SNR. GLCM-HOSVD is proposed to extract features from audio data. During classifier training, the sound samples mixed by environmental sound and sound events are used to train RF. The experiment proves that the proposed method has the ability to recognize low SNR sound events in acoustic scenes. The result shows that the accuracy rate is higher than 78% even in 5dB acoustic scene.
Starting Page	1448
Ending Page	1453
File Size	409115
Page Count	6
File Format	PDF
e-ISBN	9781467390989
DOI	10.1109/CISP.2015.7408111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Radio frequency random forests (RF) Empirical mode decomposition (EMD) Feature extraction higher-order singular value decomposition (HOSVD) Acoustics Matrix decomposition Spectrogram Signal to noise ratio gray level co-occurrence matrix (GLCM)
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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