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Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF

1
State Key Laboratory for Manufacturing Systems Engineering, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Zhou, Q.; Feng, Z. Robust sound event detection through noise estimation and source separation using NMF. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, 16–17 November 2017.
Sensors 2019, 19(14), 3206; https://doi.org/10.3390/s19143206
Received: 26 May 2019 / Revised: 16 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
(This article belongs to the Section Intelligent Sensors)
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Abstract

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). First, a scheme for noise dictionary learning from the input noisy signal is employed by the technique of robust NMF, which supports adaptation to noise variations. The estimated noise dictionary is used to develop a supervised source separation framework in combination with a pre-trained event dictionary. Second, to improve the separation quality, we extend the basic NMF model to a weighted form, with the aim of varying the relative importance of the different components when separating a target sound event from noise. With properly designed weights, the separation process is forced to rely more on those dominant event components, whereas the noise gets greatly suppressed. The proposed method is evaluated on a dataset of the rare sound event detection task of the DCASE 2017 challenge, and achieves comparable results to the top-ranking system based on convolutional recurrent neural networks (CRNNs). The proposed weighted NMF method shows an excellent noise reduction ability, and achieves an improvement of an F-score by 5%, compared to the unweighted approach. View Full-Text
Keywords: sound event detection; non-stationary noise; weighted non-negative matrix factorization; source separation sound event detection; non-stationary noise; weighted non-negative matrix factorization; source separation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhou, Q.; Feng, Z.; Benetos, E. Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF. Sensors 2019, 19, 3206.

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