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Open AccessArticle

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

1
School of Software Engineering, Payap University, Chiang Mai 50000, Thailand
2
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
3
Computer and Information Engineering Department, Ninevah University, Mosul 41002, Iraq
4
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5BN, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4368; https://doi.org/10.3390/s20164368
Received: 20 June 2020 / Revised: 1 August 2020 / Accepted: 4 August 2020 / Published: 5 August 2020
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods. View Full-Text
Keywords: audio signal processing; sound event classification; nonnegative matric factorization; blind signal separation; support vector machines audio signal processing; sound event classification; nonnegative matric factorization; blind signal separation; support vector machines
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MDPI and ACS Style

Parathai, P.; Tengtrairat, N.; Woo, W.L.; Abdullah, M.A.M.; Rafiee, G.; Alshabrawy, O. Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM. Sensors 2020, 20, 4368. https://doi.org/10.3390/s20164368

AMA Style

Parathai P, Tengtrairat N, Woo WL, Abdullah MAM, Rafiee G, Alshabrawy O. Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM. Sensors. 2020; 20(16):4368. https://doi.org/10.3390/s20164368

Chicago/Turabian Style

Parathai, Phetcharat; Tengtrairat, Naruephorn; Woo, Wai L.; Abdullah, Mohammed A.M.; Rafiee, Gholamreza; Alshabrawy, Ossama. 2020. "Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM" Sensors 20, no. 16: 4368. https://doi.org/10.3390/s20164368

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