Subband nonstationary noise reduction based on multichannel spatial prediction under reverberant environments

2009 ◽  
Author(s):  
Masahito Togami ◽  
Yohei Kawaguchi ◽  
Yasunari Obuchi
Keyword(s):  
Noise Reduction ◽  
Spatial Prediction ◽  
Reverberant Environments

scholarly journals Interactions Between Digital Noise Reduction and Reverberation: Acoustic and Behavioral Effects

2020 ◽  
Vol 31 (01) ◽  
pp. 017-029
Author(s):  
Paul Reinhart ◽  
Pavel Zahorik ◽  
Pamela Souza
Keyword(s):  
Noise Reduction ◽  
Hearing Impairment ◽  
Hearing Aids ◽  
Background Noise ◽  
Speech Intelligibility ◽  
Spectral Subtraction ◽  
Listening Effort ◽  
Speech In Noise ◽  
Reverberant Environments ◽  
Speech Naturalness

AbstractDigital noise reduction (DNR) processing is used in hearing aids to enhance perception in noise by classifying and suppressing the noise acoustics. However, the efficacy of DNR processing is not known under reverberant conditions where the speech-in-noise acoustics are further degraded by reverberation.The purpose of this study was to investigate acoustic and perceptual effects of DNR processing across a range of reverberant conditions for individuals with hearing impairment.This study used an experimental design to investigate the effects of varying reverberation on speech-in-noise processed with DNR.Twenty-six listeners with mild-to-moderate sensorineural hearing impairment participated in the study.Speech stimuli were combined with unmodulated broadband noise at several signal-to-noise ratios (SNRs). A range of reverberant conditions with realistic parameters were simulated, as well as an anechoic control condition without reverberation. Reverberant speech-in-noise signals were processed using a spectral subtraction DNR simulation. Signals were acoustically analyzed using a phase inversion technique to quantify improvement in SNR as a result of DNR processing. Sentence intelligibility and subjective ratings of listening effort, speech naturalness, and background noise comfort were examined with and without DNR processing across the conditions.Improvement in SNR was greatest in the anechoic control condition and decreased as the ratio of direct to reverberant energy decreased. There was no significant effect of DNR processing on speech intelligibility in the anechoic control condition, but there was a significant decrease in speech intelligibility with DNR processing in all of the reverberant conditions. Subjectively, listeners reported greater listening effort and lower speech naturalness with DNR processing in some of the reverberant conditions. Listeners reported higher background noise comfort with DNR processing only in the anechoic control condition.Results suggest that reverberation affects DNR processing using a spectral subtraction algorithm in such a way that decreases the ability of DNR to reduce noise without distorting the speech acoustics. Overall, DNR processing may be most beneficial in environments with little reverberation and that the use of DNR processing in highly reverberant environments may actually produce adverse perceptual effects. Further research is warranted using commercial hearing aids in realistic reverberant environments.

scholarly journals A Hybrid Coherence Model for Noise Reduction in Reverberant Environments

2015 ◽  
Vol 22 (3) ◽  
pp. 279-282 ◽  
Author(s):  
Nima Yousefian ◽  
John H. L. Hansen ◽  
Philipos C. Loizou
Keyword(s):  
Noise Reduction ◽  
Reverberant Environments

Noncausal spatial prediction filtering for random noise reduction on 3-D poststack data

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1641-1653 ◽  
Author(s):  
Necati Gülünay
Keyword(s):  
Noise Reduction ◽  
Spectral Properties ◽  
Temporal Frequency ◽  
Random Noise ◽  
Spatial Prediction ◽  
Noise Attenuation ◽  
Ratio Data ◽  
Spatial Dimensions ◽  
Prediction Filter ◽  
Better Than

A common practice in random noise reduction for 2-D data is to use pseudononcausal (PNC) 1-D prediction filters at each temporal frequency. A 1-D PNC filter is a filter that is forced to be two sided by placing a conjugate‐reversed version of a 1-D causal filter in front of itself with a zero between the two. For 3-D data, a similar practice is to solve for two 2-D (causal) one‐quadrant filters at each frequency slice. A 2-D PNC filter is formed by putting a conjugate flipped version of each quadrant filter in a quadrant opposite itself. The center sample of a 2-D PNC filter is zero. This paper suggests the use of 1-D and 2-D noncausal (NC) prediction filters instead of PNC filters for random noise attenuation, where an NC filter is a two‐sided filter solved from one set of normal equations. The number of negative and positive lags in the NC filter is the same. The center sample of the filter is zero. The NC prediction filters are more center loaded than PNC filters. They are conjugate symmetric as PNC filters. Also, NC filters are less sensitive than PNC filters to the size of the gate used in their derivation. They can handle amplitude variations along dip directions better than PNC filters. While a PNC prediction filter suppresses more random noise, it damages more signal. On the other hand, NC prediction filters preserve more of the signal and reject less noise for the same total filter length. For high S/N ratio data, a 2-D NC prediction filter preserves geologic features that do not vary in one of the spatial dimensions. In‐line and cross‐line vertical faults are also well preserved with such filters. When faults are obliquely oriented, the filter coefficients adapt to the fault. Spectral properties of PNC and NC filters are very similar.

Satisfaction With Noise Reduction Circuits

1993 ◽  
Vol 2 (1) ◽  
pp. 51-53 ◽  
Author(s):  
Ruth A. Bentler
Keyword(s):  
Noise Reduction
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