Evaluation of the sparse coding shrinkage noise reduction algorithm for the hearing impaired

Although there are numerous single-channel noise reduction strategies to improve speech perception in a noisy environment, most of them can only improve speech quality but not improve speech intelligibility for normal hearing (NH) or hearing impaired (HI) listeners. Exceptions that can improve speech intelligibility currently are only those that require a priori statistics of speech or noise. Most of the noise reduction algorithms in hearing aids are adopted directly from the algorithms for NH listeners without taking into account of the hearing loss factors within HI listeners. HI listeners suffer more in speech intelligibility than NH listeners in the same noisy environment. Further study of monaural noise reduction algorithms for HI listeners is required.The motivation is to adapt a model-based approach in contrast to the conventional Wiener filtering approach. The model-based algorithm called sparse coding shrinkage (SCS) was proposed to extract key speech information from noisy speech. The SCS algorithm was evaluated by comparison with another state-of-the-art Wiener filtering approach through speech intelligibility and quality tests using 9 NH and 9 HI listeners. The SCS algorithm matched the performance of the Wiener filtering algorithm in speech intelligibility and speech quality. Both algorithms showed some intelligibility improvements for HI listeners but not at all for NH listeners. The algorithms improved speech quality for both HI and NH listeners.Additionally, a physiologically-inspired hearing loss simulation (HLS) model was developed to characterize hearing loss factors and simulate hearing loss consequences. A methodology was proposed to evaluate signal processing strategies for HI listeners with the proposed HLS model and NH subjects. The corresponding experiment was performed by asking NH subjects to listen to unprocessed/enhanced speech with the HLS model. Some of the effects of the algorithms seen in HI listeners are reproduced, at least qualitatively, by using the HLS model with NH listeners.Conclusions: The model-based algorithm SCS is promising for improving performance in stationary noise although no clear difference was seen in the performance of SCS and a competitive Wiener filtering algorithm. Fluctuating noise is more difficult to reduce compared to stationary noise. Noise reduction algorithms may perform better at higher input signal-to-noise ratios (SNRs) where HI listeners can get benefit but where NH listeners already reach ceiling performance. The proposed HLS model can save time and cost when evaluating noise reduction algorithms for HI listeners

Speech quality evaluation of a sparse coding shrinkage noise reduction algorithm with normal hearing and hearing impaired listeners

Although there are numerous papers describing single-channel noise reduction strategies to improve speech perception in a noisy environment, few studies have comprehensively evaluated the effects of noise reduction algorithms on speech quality for hearing impaired (HI). A model-based sparse coding shrinkage (SCS) algorithm has been developed, and has shown previously (Sang et al., 2014) that it is as competitive as a state-of-the-art Wiener filter approach in speech intelligibility. Here, the analysis is extended to include subjective quality ratings and a method called Interpolated Paired Comparison Rating (IPCR) is adopted to quantitatively link the benefit of speech intelligibility and speech quality.The subjective quality tests are performed through IPCR to efficiently quantify noise reduction effects on speech quality. Objective measures including frequency-weighted segmental signal-to-noise ratio (fwsegSNR), perceptual evaluation of speech quality (PESQ) and hearing aid speech quality index (HASQI) are adopted to predict the noise reduction effects.Results show little difference in speech quality between the SCS and the Wiener filter algorithm but a difference in quality rating between the HI and NH listeners. HI listeners generally gave better quality ratings of noise reduction algorithms than NH listeners. However, SCS reduced the noise more efficiently at the cost of higher distortions that were detected by NH but not by the HI.SCS is a promising candidate for noise reduction algorithms for HI. In general, care needs to be taken when adopting algorithms that were originally developed for NH participants into hearing aid applications. An algorithm that is evaluated negatively with NH might still bring benefits for HI participant

Simulation of hearing loss using compressive Gammachirp auditory filters

Hearing loss simulation (HLS) systems can provide normal hearing (NH) listeners with demonstrations of consequences of hearing impairment. An accurate simulation of hearing loss can be a valuable tool for developing signal processing strategies for hearing aids. This paper presents a novel HLS system which is based on a physiologically motivated compressive gammachirp auditory filter bank to simulate several aspects of hearing loss including elevated hearing threshold, loudness recruitment and reduced frequency selectivity. The model was evaluated by speech-in-noise tests. An experiment with normally hearing and hearing-impaired listeners showed that the proposed HLS model can mimic typical hearing loss. It is concluded that a physiologically-inspired hearing loss model can perform in the same way as phenomenological models, yet has more fundamental underpinnin

Evaluation of headphone phase equalization on sound reproduction

Headphone equalization can help to deliver high perceptual sound quality. The headphone-to-eardrum transfer function (HETF) equalization can be used to control magnitude and phase responses of headphones. Magnitude equalization is still elusive on how to set a preferred target response while phase equalization is an important but overlooked method. Here the targets of magnitude equalization and phase equalization on headphone reproduction were evaluated through objective and subjective experiments. The objective evaluation shows that the linearity of phase response is important for the decay speed of the corresponding transient response. The subjective experiments show that the target of a linear phase response, but not a flat magnitude response, is able to significantly improve headphone reproduction quality in sound clarity

Supervised sparse coding strategy in cochlear implants

In this paper we explore how to improve a sparse coding (SC) strategy that was successfully used to improve subjective speech perception in noisy environment in cochlear implants. On the basis of the existing unsupervised algorithm, we developed an enhanced supervised SC strategy, using the SC shrinkage (SCS) principle. The new algorithm is implemented at the stage of the spectral envelopes after the signal separation in a 22-channel filter bank. SCS can extract and transmit the most important information from noisy speech. The new algorithm is compared with the unsupervised algorithm using objective evaluation for speech in babble and white noise (signal-to-noise ratios, SNR = 10dB, 5dB, 0dB) using objective measures in a cochlea implant simulation. Results show that the supervised SC strategy performs better in white noise, but not significantly better with babble noise.</p