Topic
Noise reduction
About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.
Papers published on a yearly basis
Papers
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04 May 2014
TL;DR: The proposed Long Short-Term Memory recurrent neural networks are trained to predict clean speech as well as noise features from noisy speech features, and a magnitude domain soft mask is constructed from these features, which outperforms unsupervised magnitude domain spectral subtraction by a large margin in terms of source-distortion ratio.
Abstract: In this paper we propose the use of Long Short-Term Memory recurrent neural networks for speech enhancement. Networks are trained to predict clean speech as well as noise features from noisy speech features, and a magnitude domain soft mask is constructed from these features. Extensive tests are run on 73 k noisy and reverberated utterances from the Audio-Visual Interest Corpus of spontaneous, emotionally colored speech, degraded by several hours of real noise recordings comprising stationary and non-stationary sources and convolutive noise from the Aachen Room Impulse Response database. In the result, the proposed method is shown to provide superior noise reduction at low signal-to-noise ratios while creating very little artifacts at higher signal-to-noise ratios, thereby outperforming unsupervised magnitude domain spectral subtraction by a large margin in terms of source-distortion ratio.
135 citations
Journal Article•
TL;DR: A simple method for the cosmetic removal of scan-line noise from geometrically corrected Landsat Thematic Mapper data is presented and the possible effects upon image signal are discussed.
Abstract: A simple method for the cosmetic removal of scan-line noise from geometrically corrected Landsat Thematic Mapper data is presented. The method uses only standard spatial filters and arithmetic routines that are already present on most image processing systems. Examples are provided, and the possible effects upon image signal are discussed.
134 citations
TL;DR: A NILM algorithm based on the Deep Neural Networks is proposed, which outperforms the AFAMAP algorithm both in seen and unseen condition, and that it exhibits a significant robustness in presence of noise.
134 citations
TL;DR: In this article, a new online secondary path modeling method with auxiliary noise power scheduling and adaptive filter norm manipulation is proposed to alleviate the increment of the residual noise due to the auxiliary noise.
Abstract: In many practical cases for active noise control (ANC), the online secondary path modeling methods that use auxiliary noise are often applied. However, the auxiliary noise contributes to residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, a sudden and large change in the secondary path leads to easy divergence of the existing online secondary path modeling methods. To mitigate these problems, this paper proposes a new online secondary path modeling method with auxiliary noise power scheduling and adaptive filter norm manipulation. The auxiliary noise power is scheduled based on the convergence status of an ANC system with consideration of the variation of the primary noise. The purpose is to alleviate the increment of the residual noise due to the auxiliary noise. In addition, the norm manipulation is applied to adaptive filters in the ANC system. The objective is to avoid over-updates of adaptive filters due to the sudden large change in the secondary path and thus prevent the ANC system from diverging. Computer simulations show the effectiveness and robustness of the proposed method.
134 citations