Speech Enhancement

Hansen, John H. L.

doi:10.1002/047134608x.w6710

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…The perceived quality of the signal emerging from a hearing aid is affected by noise present in the input signal ͑e.g., speech in a noisy background͒ as well as by noise and distortion within the hearing aid itself. Digital signal-processing algorithms in hearing aids are often nonlinear and generate unwanted distortion along with the desired signal modifications ͑e.g., Kates, 1993;Hansen, 1999;Stelmachowicz et al, 1999;Souza et al, 2006͒. Furthermore, the hearing-aid circuits and transducers generate additional nonlinear distortion that can reduce sound quality ͑Palmer et al, 1995͒.…”

Section: Introductionmentioning

confidence: 99%

Effects of noise and distortion on speech quality judgments in normal-hearing and hearing-impaired listeners

Arehart

Kates

Anderson

et al. 2007

The Journal of the Acoustical Society of America

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Noise and distortion reduce speech intelligibility and quality in audio devices such as hearing aids. This study investigates the perception and prediction of sound quality by both normal-hearing and hearing-impaired subjects for conditions of noise and distortion related to those found in hearing aids. Stimuli were sentences subjected to three kinds of distortion (additive noise, peak clipping, and center clipping), with eight levels of degradation for each distortion type. The subjects performed paired comparisons for all possible pairs of 24 conditions. A one-dimensional coherence-based metric was used to analyze the quality judgments. This metric was an extension of a speech intelligibility metric presented in Kates and Arehart (2005) [J. Acoust. Soc. Am. 117, 2224-2237] and is based on dividing the speech signal into three amplitude regions, computing the coherence for each region, and then combining the three coherence values across frequency in a calculation based on the speech intelligibility index. The one-dimensional metric accurately predicted the quality judgments of normal-hearing listeners and listeners with mild-to-moderate hearing loss, although some systematic errors were present. A multidimensional analysis indicates that several dimensions are needed to describe the factors used by subjects to judge the effects of the three distortion types.

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Section: Introductionmentioning

confidence: 99%

Effects of noise and distortion on speech quality judgments in normal-hearing and hearing-impaired listeners

Arehart

Kates

Anderson

et al. 2007

The Journal of the Acoustical Society of America

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show abstract

“…For our evaluation, we considered two types of noise with different frequency and temporal structure: (i) stationary flat communications channel noise (FLN), and (ii) large crowd noise from within an open room (LCR). These noise sources have previously been used for speech enhancement and robust speech recognition evaluations [22]. The FLN noise represents a broadband noise source that is quite stationary.…”

Section: Discussionmentioning

confidence: 99%

“…The performance of an enhancement algorithm can be assessed in two ways: (a) employing objective speech quality measures and/or (b) subjective listener tests, which have as their goal to quantify the improvement/distortion that a human listener would perceive. Two of the most widely used objective quality measures are the segmental SNR (SegSNR) and the Itakura-Saito (IS) distance measure [21,22]. In normal-hearing listeners, the SegSNR and IS measures have been benchmarked against subjective speech quality measures such as the diagnostic acceptability measure (DAM).…”

Section: Objective Quality Measuresmentioning

confidence: 99%

An Auditory-Masking-Threshold-Based Noise Suppression Algorithm GMMSE-AMT[ERB] for Listeners with Sensorineural Hearing Loss

Natarajan

Hansen

Arehart

et al. 2005

EURASIP J. Adv. Signal Process.

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This study describes a new noise suppression scheme for hearing aid applications based on the auditory masking threshold (AMT) in conjunction with a modified generalized minimum mean square error estimator (GMMSE) for individual subjects with hearing loss. The representation of cochlear frequency resolution is achieved in terms of auditory filter equivalent rectangular bandwidths (ERBs). Estimation of AMT and spreading functions for masking are implemented in two ways: with normal auditory thresholds and normal auditory filter bandwidths (GMMSE-AMT[ERB]-NH) and with elevated thresholds and broader auditory filters characteristic of cochlear hearing loss (GMMSE-AMT[ERB]-HI). Evaluation is performed using speech corpora with objective quality measures (segmental SNR, Itakura-Saito), along with formal listener evaluations of speech quality rating and intelligibility. While no measurable changes in intelligibility occurred, evaluations showed quality improvement with both algorithm implementations. However, the customized formulation based on individual hearing losses was similar in performance to the formulation based on the normal auditory system.

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“…In the past, a number of single-microphone speech enhancement algorithms have been proposed. A number of these are discussed in overview studies by Lim and Oppenheim [15], Ephraim [3], and Hansen [7]. These include variants of spectral subtraction [2], methods based on all-pole modeling [8], [15], subspace model based methods [5], [14], schemes based on hidden Markov models [4], [25], and algorithms that exploit masking effects, [26], [27].…”

Section: Introductionmentioning

confidence: 99%

Speech enhancement using a constrained iterative sinusoidal model

Jensen

Hansen

2001

IEEE Trans. Speech Audio Process.

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This paper presents a sinusoidal model based algorithm for enhancement of speech degraded by additive broad-band noise. In order to ensure speech-like characteristics observed in clean speech, smoothness constraints are imposed on the model parameters using a spectral envelope surface (SES) smoothing procedure. Algorithm evaluation is performed using speech signals degraded by additive white Gaussian noise. Distortion as measured by objective speech quality scores showed a 34%-41% reduction over a SNR range of 5-to-20 dB. Objective and subjective evaluations also show considerable improvement over traditional spectral subtraction and Wiener filtering based schemes. Finally, in a subjective AB preference test, where enhanced signals were coded with the G729 codec, the proposed scheme was preferred over the traditional enhancement schemes tested for SNR's in the range of 5 to 20 dB.

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Speech Enhancement

Abstract: The sections in this article are Overview Short‐Term Spectral Amplitude Methods Speech Modeling and Wiener Filtering Adaptive Noise Canceling Methods Based on Fundamental Frequency Tracking Future Directions for Speech Enhancement

Cited by 6 publications

References 40 publications

Effects of noise and distortion on speech quality judgments in normal-hearing and hearing-impaired listeners

Effects of noise and distortion on speech quality judgments in normal-hearing and hearing-impaired listeners

An Auditory-Masking-Threshold-Based Noise Suppression Algorithm GMMSE-AMT[ERB] for Listeners with Sensorineural Hearing Loss

Speech enhancement using a constrained iterative sinusoidal model

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