Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks

An adaptive noise sensing method is proposed to improve the speech sensing performance of speech-based applications operated over wireless sensor networks. The proposed method is based on nonnegative matrix factorization (NMF), which consists of adaptive noise sensing and noise reduction. In other w...

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Main Authors: Kwang Myung Jeon, Hong Kook Kim, Sung Joo Lee, Yun Keun Lee
Format: Article
Language:English
Published: Wiley 2014-04-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2014/640915
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author Kwang Myung Jeon
Hong Kook Kim
Sung Joo Lee
Yun Keun Lee
author_facet Kwang Myung Jeon
Hong Kook Kim
Sung Joo Lee
Yun Keun Lee
author_sort Kwang Myung Jeon
collection DOAJ
description An adaptive noise sensing method is proposed to improve the speech sensing performance of speech-based applications operated over wireless sensor networks. The proposed method is based on nonnegative matrix factorization (NMF), which consists of adaptive noise sensing and noise reduction. In other words, adaptive noise sensing is performed by adapting a priori noise basis matrix of the NMF, which is estimated from the noise signal, resulting in an adapted noise basis matrix. Subsequently, the adapted noise basis matrix is used for the NMF decomposition of noisy speech into clean speech and background noise. The estimated clean speech signal is then applied to a front-end of the speech-based applications. The performance of the proposed NMF-based noise sensing and reduction method is first evaluated by measuring the source to distortion ratio (SDR), the source to interferences ratio (SIR), and the source to artifacts ratio (SAR). In addition, the proposed method is applied to an automatic speech recognition (ASR) system, which is a typical speech-based application, and then the average word error rate (WER) of the ASR is compared with that employing either a Wiener filter, or a conventional NMF-based noise reduction method using only a priori noise basis matrix.
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spelling doaj-art-8d144cb2ab8f4e4182e3036c48b45dd42025-08-20T03:07:02ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-04-011010.1155/2014/640915640915Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor NetworksKwang Myung Jeon0Hong Kook Kim1Sung Joo Lee2Yun Keun Lee3 School of Information and Communications, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 500-712, Republic of Korea School of Information and Communications, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 500-712, Republic of Korea Speech/Language Information Research Center, Electronics and Telecommunications Research Institute (ETRI), 138 Gajeonggno, Yuseong-gu, Daejeon 305-700, Republic of Korea Speech/Language Information Research Center, Electronics and Telecommunications Research Institute (ETRI), 138 Gajeonggno, Yuseong-gu, Daejeon 305-700, Republic of KoreaAn adaptive noise sensing method is proposed to improve the speech sensing performance of speech-based applications operated over wireless sensor networks. The proposed method is based on nonnegative matrix factorization (NMF), which consists of adaptive noise sensing and noise reduction. In other words, adaptive noise sensing is performed by adapting a priori noise basis matrix of the NMF, which is estimated from the noise signal, resulting in an adapted noise basis matrix. Subsequently, the adapted noise basis matrix is used for the NMF decomposition of noisy speech into clean speech and background noise. The estimated clean speech signal is then applied to a front-end of the speech-based applications. The performance of the proposed NMF-based noise sensing and reduction method is first evaluated by measuring the source to distortion ratio (SDR), the source to interferences ratio (SIR), and the source to artifacts ratio (SAR). In addition, the proposed method is applied to an automatic speech recognition (ASR) system, which is a typical speech-based application, and then the average word error rate (WER) of the ASR is compared with that employing either a Wiener filter, or a conventional NMF-based noise reduction method using only a priori noise basis matrix.https://doi.org/10.1155/2014/640915
spellingShingle Kwang Myung Jeon
Hong Kook Kim
Sung Joo Lee
Yun Keun Lee
Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks
International Journal of Distributed Sensor Networks
title Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks
title_full Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks
title_fullStr Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks
title_full_unstemmed Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks
title_short Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks
title_sort nonnegative matrix factorization based adaptive noise sensing over wireless sensor networks
url https://doi.org/10.1155/2014/640915
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AT hongkookkim nonnegativematrixfactorizationbasedadaptivenoisesensingoverwirelesssensornetworks
AT sungjoolee nonnegativematrixfactorizationbasedadaptivenoisesensingoverwirelesssensornetworks
AT yunkeunlee nonnegativematrixfactorizationbasedadaptivenoisesensingoverwirelesssensornetworks