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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
18 Jun 2008
TL;DR: In this article, the perceived loudness of an audio signal is measured by modifying a spectral representation of the audio signal as a function of a reference spectral shape so that the spectral representation conforms more closely to the reference signal spectral shape.
Abstract: The perceived loudness of an audio signal is measured by modifying a spectral representation of an audio signal as a function of a reference spectral shape so that the spectral representation of the audio signal conforms more closely to the reference spectral shape, and determining the perceived loudness of the modified spectral representation of the audio signal.

27 citations

Patent
20 Nov 2006
TL;DR: In this paper, a noisy audio signal, with user input device noise, is received, and particular frames in the audio signal are identified and removed. The removed audio data is then reconstructed to obtain a clean audio signal.
Abstract: A noisy audio signal, with user input device noise, is received. Particular frames in the audio signal that are corrupted by user input device noise are identified and removed. The removed audio data is then reconstructed to obtain a clean audio signal.

27 citations

Book ChapterDOI
01 Jan 2009
TL;DR: The proposed system enables automated detection of epileptic seizures, which can have a large positive impact on the daily care of epilepsy patients, and is tested with audio signals obtained from measurements with epileptic patients.
Abstract: Patients that undergo treatment in the epilepsy clinic Kempenhaeghe in the Netherlands are being monitored with different sensory signals, including audio. In this paper a new patient monitoring system for the detection of epileptic seizures through audio classification is proposed. The proposed system enables automated detection of epileptic seizures, which can have a large positive impact on the daily care of epilepsy patients. This system includes three stages. First, the signal is enhanced by means of a microphone array, followed by a noise subtraction procedure. Secondly, the signal is analyzed by audio event detection and audio classification. When an audio event is detected, features are extracted from the signal. Bayesian decision theory is used to classify the feature vector based on a discriminant analysis. Finally, it is decided whether or not to trigger an alarm. The performance is tested with audio signals obtained from measurements with epileptic patients. The results show that, with a limited set of features, good classification results can be achieved.

27 citations

Patent
Narayan Biswal1, Gokcen Cilingir1
29 Mar 2016
TL;DR: In this paper, a speech to noise ratio and noise type label corresponding to received audio is used to determine an adaptive speaker recognition threshold and a speaker recognition score corresponding to the received audio.
Abstract: Techniques related to speaker recognition are discussed. Such techniques may include determining an adaptive speaker recognition threshold based on a speech to noise ratio and noise type label corresponding to received audio and performing speaker recognition based on the adaptive speaker recognition threshold and a speaker recognition score corresponding to received audio.

27 citations

Journal ArticleDOI
07 Feb 2013-PLOS ONE
TL;DR: An impaired ability to efficiently process envelope and fine structure cues of the speech signal may be the cause of the extreme difficulties faced during speech perception in noise by listeners with Auditory Neuropathy.
Abstract: Aim The present study evaluated the relation between speech perception in the presence of background noise and temporal processing ability in listeners with Auditory Neuropathy (AN). Method The study included two experiments. In the first experiment, temporal resolution of listeners with normal hearing and those with AN was evaluated using measures of temporal modulation transfer function and frequency modulation detection at modulation rates of 2 and 10 Hz. In the second experiment, speech perception in quiet and noise was evaluated at three signal to noise ratios (SNR) (0, 5, and 10 dB). Results Results demonstrated that listeners with AN performed significantly poorer than normal hearing listeners in both amplitude modulation and frequency modulation detection, indicating significant impairment in extracting envelope as well as fine structure cues from the signal. Furthermore, there was significant correlation seen between measures of temporal resolution and speech perception in noise. Conclusion Results suggested that an impaired ability to efficiently process envelope and fine structure cues of the speech signal may be the cause of the extreme difficulties faced during speech perception in noise by listeners with AN.

27 citations


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