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Noise

About: Noise is a research topic. Over the lifetime, 5111 publications have been published within this topic receiving 69407 citations. The topic is also known as: Мопсы танцуют под радио бандитов из сталкера 10 часов.


Papers
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Patent
Jes Thyssen1, Elias Nemer1
22 Dec 2011
TL;DR: In this paper, a system and methods for detecting and suppressing breathing noise in an audio signal are described, which leverage the multiple microphones to suppress detected breathing noises in a manner that minimizes signal distortion.
Abstract: Systems and methods are described herein for detecting and suppressing breathing noise in an audio signal. First, systems and methods are described that analyze audio signals generated by two or more microphones to detect breathing noise in one of the audio signals and that leverage the multiple microphones to suppress detected breathing noise in a manner that minimizes signal distortion. Then, systems and methods are described that are capable of analyzing the audio signal generated by a single microphone to detect breathing noise in the audio signal and thereafter suppress it.

17 citations

Journal ArticleDOI
TL;DR: Three adult male stutterers spoke spontaneously during a series of base-rate and noise sessions to study the impact of noise and base rate on the ability to speak spontaneously.
Abstract: Three adult male stutterers spoke spontaneously during a series of base-rate and noise sessions. Base rate was run first for a minimum of 100 minutes and until two criteria of stability were met. F...

17 citations

Proceedings ArticleDOI
06 Jul 2003
TL;DR: An adaptive noise suppression system that mitigates or eliminates processing artifacts common to Wiener filtering without decreasing speech recognition performance is proposed.
Abstract: Removal of ambient noise from a single-channel audio signal is becoming an increasingly important problem due to the proliferation of portable communication devices. Furthermore, in applications such as wireless telephony and phonetic data mining, it is desired that noise suppression be robust to changing noise conditions and that processing take place in real time or faster. This paper proposes an adaptive noise suppression system that mitigates or eliminates processing artifacts common to Wiener filtering without decreasing speech recognition performance. Results of one implementation of such a structure demonstrate significant improvements in both perceptual quality and speech recognition performance under noisy conditions.

17 citations

Patent
30 Nov 1973
TL;DR: In this article, a digital squelch circuit responds to a band limited audio output signal of a radio receiver to count the number of cycles of the source per unit length of time.
Abstract: A digital squelch circuit responds to a band limited audio output signal of a radio receiver to count the number of cycles of the source per unit length of time. In response to the count being respectively above and below a predetermined value, the audio signal source is decoupled from and gated to an output terminal. The audio signal source is gated to the output terminal for a predetermined period subsequent to termination of the count with the predetermined time being less than a pre-established value in order to prevent squelching in the period between adjacent words of a voice source.

17 citations

Proceedings ArticleDOI
22 Nov 2013
TL;DR: A method for reducing noise from audio or speech signals using LMS adaptive filtering algorithm is proposed, where the signal is filtered in the time domain, while the filter coefficients are calculated adaptively by steepest-descent algorithm.
Abstract: Noise reduction of audio signals is a key challenge problem in speech enhancement, speech recognition and speech communication applications, etc. It has attracted a considerable amount of research attention over past several decades. The most widely used method is optimal linear filtering method, which achieves clean audio estimate by passing the noise observation through an optimal linear filter or transformation. The representative algorithms include Wiener filtering, Kalman filtering, spectral restoration, subspace method, etc. Many theoretical analysis and experiments have been carried out to show that the optimal filtering technique can reduce the level of noise that is present in the audio signals and improve the corresponding signal-to-noise ratio (SNR). However, one of the main problems for optimal filtering method is complexity of the algorithm which based upon SVD–decompositions or QR–decompositions. In almost real signal applications it difficult to implement. In this paper, a method for reducing noise from audio or speech signals using LMS adaptive filtering algorithm is proposed. The signal is filtered in the time domain, while the filter coefficients are calculated adaptively by steepest-descent algorithm. The simulation results exhibit a higher quality of the processed signal than unprocessed signal in the noise situation. 1 Y. Liu () College of Electronic Information Engineering, Inner Mongolia University, 010021, Hohhot, China e-mail: yangliuimu@163.com Y. Liu Faculty of Electronic Information and Electrical engineering, Dalian University of Technology, Dalian, China M. Xiao College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China Y. Tie College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China 3rd International Conference on Multimedia Technology(ICMT 2013) © 2013. The authors Published by Atlantis Press 1001

17 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
2021125
2020217
2019224
2018243
2017214