On-line frame-synchronous compensation of non-stationary noise

Barreaud, Vincent; Illina, Irina; Fohr, Dominique

doi:10.1109/icassp.2003.1198865

Cited by 3 publications

(1 citation statement)

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“…However, it is difficult to remove such noises because we do not know where the noise overlapped and what the noise was. Many studies have been conducted on non-stationary noise reduction in a single chan- nel [1,2]. But it is difficult for these methods to track sudden noises.…”

Section: Introductionmentioning

confidence: 99%

Noise Detection and Classification in Speech Signals with Boosting

Miyake

Takiguchi

Ariki

2007

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

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This paper presents a novel method to detect and classify sudden noises in speech signals. There are many sudden and short-period noises in natural environments, such as inside a car. If a speech recognition system can detect sudden noises, it will make it possible for the system to ask the speaker to repeat the same utterance so that the speech data will be clean. If clean speech data can be input, it will help prevent system operation errors. In this paper, we tried to detect and classify sudden noises in user's utterances using Boosting. Boosting can create a complex, non-linear boundary that determines whether the observed signal is speech, noise1, noise2, or so on. In our experiments, the proposed method achieved good performance in comparison to a conventional method based on the GMM (Gaussian Mixture Model).

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Section: Introductionmentioning

confidence: 99%

Noise Detection and Classification in Speech Signals with Boosting

Miyake

Takiguchi

Ariki

2007

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

View full text Add to dashboard Cite

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On-line compensation for non-stationary noise

Barreaud

Illina

Fohr

2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721)

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An improvement of a previously proposed frame-synchronous noise compensation algorithm is presented. The previous algorithm used Stochastic Matching approach and was designed to cope with time-varying noise. The improved version of this algorithm takes into account the abrupt changes.. 'in the acoustical environment. The environment change is detected using the Shewart Control Charts detection algorithm that searches for the changes in the means of Gaussian sequence. For each noisy environment a specific mismatch function is estimated. A simple bias is used as mismatch function. After detection of environment change, the bias is reinitialized by the value corresponding to the closest noisy environment previously observed. This technique is general since no assumption is made on the nature, level and occumng time of noise. Our algorithm is evaluated on the VODIS database (speech in moving car). All the test sentences were corrupted by an additive noise occurring at the middle of each sentence. This latest version of our algorithm gives significant improvement compared to classical compensation algorithms.

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