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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
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Journal ArticleDOI
TL;DR: This paper addresses the problem of single channel speech enhancement at very low signal-to-noise ratios (SNRs) (<10 dB) with a new computationally efficient algorithm developed based on masking properties of the human auditory system, resulting in improved results over classical subtractive-type algorithms.
Abstract: This paper addresses the problem of single channel speech enhancement at very low signal-to-noise ratios (SNRs) (<10 dB). The proposed approach is based on the introduction of an auditory model in a subtractive-type enhancement process. Single channel subtractive-type algorithms are characterized by a tradeoff between the amount of noise reduction, the speech distortion, and the level of musical residual noise, which can be modified by varying the subtraction parameters. Classical algorithms are usually limited to the use of fixed optimized parameters, which are difficult to choose for all speech and noise conditions. A new computationally efficient algorithm is developed based on masking properties of the human auditory system. It allows for an automatic adaptation in time and frequency of the parametric enhancement system, and finds the best tradeoff based on a criterion correlated with perception. This leads to a significant reduction of the unnatural structure of the residual noise. Objective and subjective evaluation of the proposed system is performed with several noise types form the Noisex-92 database, having different time-frequency distributions. The application of objective measures, the study of the speech spectrograms, as well as subjective listening tests, confirm that the enhanced speech is more pleasant to a human listener. Finally, the proposed enhancement algorithm is tested as a front-end processor for speech recognition in noise, resulting in improved results over classical subtractive-type algorithms.

631 citations

01 Jan 1992
TL;DR: In this paper, the authors present a set of criteria for hearing and human body Vibration in buildings and communities, based on the American System of Units (ASU), and evaluate the damage risk of these criteria.
Abstract: Preface. Contributors. 1. Basic Acoustical Quantities: Levels and Decibels (Leo L. Beranek). 2. Waves and Impedances (Leo L. Beranek). 3. Data Analysis (Allan G. Piersol). 4. Determination of Sound Power Levels and Directivity of Noise Sources (William W. Lang, George C. Maling, Jr., Matthew A. Nobile, and Jiri Tichy). 5. Outdoor Sound Propagation (Ulrich J. Kurze and Grant S. Anderson). 6. Sound in Small Enclosures (Donald J. Nefske and Shung H. Sung). 7. Sound in Rooms (Murray Hodgson and John Bradley). 8. Sound-Absorbing Materials and Sound Absorbers (Keith Attenborough and Istvan L. Ver). 9. Passive Silencers (M. L. Munjal, Anthony G. Galaitsis and Istvan L. Ver). 10. Sound Generation (Istvan L. Ver). 11. Interaction of Sound Waves with Solid Structures (Istvan L. Ver). 12. Enclosures, Cabins, and Wrappings (Istvan L. Ver). 13. Vibration Isolation (Eric E. Ungar and Jeffrey A. Zapfe). 14. Structural Damping (Eric E. Ungar and Jeffrey A. Zapfe). 15. Noise of Gas Flows (H. D. Baumann and W. B. Coney). 16. Prediction of Machinery Noise (Eric W. Wood and James D. Barnes). 17. Noise Control in Heating, Ventilating, and Air Conditioning Systems (Alan T. Fry and Douglas H. Sturz). 18. Active Control of Noise and Vibration (Ronald Coleman and Paul J. Remington). 19. Damage Risk Criteria for Hearing and Human Body Vibration (Suzanne D. Smith, Charles W. Nixon and Henning E. Von Gierke). 20. Criteria for Noise in Buildings and Communities (Leo L. Beranek). 21. Acoustical Standards for Noise and Vibration Control (Angelo Campanella, Paul Schomer and Laura Ann Wilber). Appendix A. General References. Appendix B. American System of Units. Appendix C. Conversion Factors. Index.

623 citations

Journal ArticleDOI
TL;DR: FFDNet as mentioned in this paper proposes a fast and flexible denoising convolutional neural network with a tunable noise level map as the input, which can handle a wide range of noise levels effectively with a single network.
Abstract: Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including (i) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network, (ii) the ability to remove spatially variant noise by specifying a non-uniform noise level map, and (iii) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.

602 citations

Journal ArticleDOI
TL;DR: A novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage, which compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio and of perceived image quality.
Abstract: We propose a novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage. It follows the structure of the block-matching 3-D algorithm, recently proposed for additive white Gaussian noise denoising, but modifies its major processing steps in order to take into account the peculiarities of SAR images. A probabilistic similarity measure is used for the block-matching step, while the wavelet shrinkage is developed using an additive signal-dependent noise model and looking for the optimum local linear minimum-mean-square-error estimator in the wavelet domain. The proposed technique compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio (on simulated speckled images) and of perceived image quality.

601 citations

Journal ArticleDOI
TL;DR: In this article, a rank reduction algorithm for simultaneous reconstruction and random noise attenuation of seismic records is proposed, which is based on multichannel singular spectrum analysis (MSSA).
Abstract: We present a rank reduction algorithm that permits simultaneous reconstruction and random noise attenuation of seismic records. We based our technique on multichannel singular spectrum analysis (MSSA). The technique entails organizing spatial data at a given temporal frequency into a block Hankel matrix that in ideal conditions is a matrix of rank k , where k is the number of plane waves in the window of analysis. Additive noise and missing samples will increase the rank of the block Hankel matrix of the data. Consequently, rank reduction is proposed as a means to attenuate noise and recover missing traces. We present an iterative algorithm that resembles seismic data reconstruction with the method of projection onto convex sets. In addition, we propose to adopt a randomized singular value decomposition to accelerate the rank reduction stage of the algorithm. We apply MSSA reconstruction to synthetic examples and a field data set. Synthetic examples were used to assess the performance of the method in two...

598 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,511
20222,974
20211,123
20201,488
20191,702
20181,631