A two-step noise reduction technique

Plapous, Cyril; Marro, Claude; Mauuary, Laurent; Scalart, Pascal

doi:10.1109/icassp.2004.1325979

Cited by 63 publications

(36 citation statements)

References 4 publications

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“…This is further explored by Cohen (2005), and is used in a modified form in ETSI (2002); Plapous et al (2004). Whilst these approaches are beyond the scope of the present study, the proposed approach does not preclude using them.…”

Section: Enhancementmentioning

confidence: 99%

Cepstral normalisation and the signal to noise ratio spectrum in automatic speech recognition

Garner

2011

Speech Communication

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Cepstral normalisation in automatic speech recognition is investigated in the context of robustness to additive noise. In this paper, it is argued that such normalisation leads naturally to a speech feature based on signal to noise ratio rather than absolute energy (or power). Explicit calculation of this SNR-cepstrum by means of a noise estimate is shown to have theoretical and practical advantages over the usual (energy based) cepstrum. The relationship between the SNR-cepstrum and the articulation index, known in psycho-acoustics, is discussed. Experiments are presented suggesting that the combination of the SNR-cepstrum with the well known perceptual linear prediction method can be beneficial in noisy environments.

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Section: Enhancementmentioning

confidence: 99%

Cepstral normalisation and the signal to noise ratio spectrum in automatic speech recognition

Garner

2011

Speech Communication

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“…We note that in the Decision Directed estimator of [3], the ML estimate of ξ of (13) is regularised using an estimate based on the previous spectral magnitude estimate. This is further explored by Cohen [10], and is used in a modified form in [4,11]. Whilst these approaches are beyond the scope of the present study, our approach does not preclude using them.…”

Section: Snr Estimatementioning

confidence: 99%

SNR features for automatic speech recognition

Garner

2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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When combined with cepstral normalisation techniques, the features normally used in Automatic Speech Recognition are based on Signal to Noise Ratio (SNR). We show that calculating SNR from the outset, rather than relying on cepstral normalisation to produce it, gives features with a number of practical and mathematical advantages over power-spectral based ones. In a detailed analysis, we derive Maximum Likelihood and Maximum a-Posteriori estimates for SNR based features, and show that they can outperform more conventional ones, especially when subsequently combined with cepstral variance normalisation. We further show anecdotal evidence that SNR based features lend themselves well to noise estimates based on low-energy envelope tracking.

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“…A several one-, two-and multichannel sensors techniques are proposed to deal with this problem. For example in [14][15][16][17], several single and two-sensor techniques are proposed to correct these distortions [18][19][20]. More advanced techniques are then proposed recently in [21,22].…”

Section: Introductionmentioning

confidence: 99%