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

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici

Quantitative Analyses Of Acceptable Noise Level For Air Conduction Listening

This study was conducted to develop quantitative models for Acceptable Noise Level (ANL) under air conduction (AC) listening conditions. Experimental results on the effects of frequency bandwidths on ANL under two listening conditions involving earphones and loudspeaker (sound field) with high and low frequencies and babble noise and white noise revealed: (a) there are statistically significant interactions among the background noise types, the background noise frequency bandwidths and signal source; (b) background noise and noise frequency bandwidths have effects on listener discriminability bias toward the noise and the signal intensity; (c) different listening conditions had different ANL thresholds; and (d) a significant difference existed between listeners\u27 Minimum ANL threshold under earphone listening and air conduction listening. The findings revealed that ANLs at different loudspeaker locations were not significantly different statistically from one another. The psychophysical parameters revealed that males had a higher positive discriminability bias toward signal and noise intensities at all locations, except at the 315 degree azimuth; female listeners had higher discriminability biases (β) toward sound at the 315 degree azimuth. For example, the β value for males under signal alone was 0.2095 compared to females\u27 value of 0.23 at the 315 degree location. Under noise only, male β values were all superior to those of females with values higher than 0.22 against less than 0.1 for females at the 180-, 225-, and 315-degree locations. The result showed that the minimum ANL threshold and the listeners\u27 discriminability biases toward sound could be found at the 315-degree loudspeaker location. Finally, a study to determine the differences between ANL and Speech Comprehension in Noise Level (SCNL) was not significant. However, the sensitivity toward sound intensity was higher under ANL than SCNL. This is because ANL is the willingness to work in noisy conditions while SCNL seeks meaning out of signals

The role of temporal fine structure in sound quality perception

Speech perception depends on access to spectral and temporal acoustic cues. Temporal cues include slowly-varying amplitude changes (temporal envelope) and higher-rate amplitude changes (temporal fine structure, TFS). This study sought to quantify the effects of alterations to temporal structure on the perception of speech quality by parametrically varying the amount of TFS available in specific frequency regions. The three research aims were to: 1) establish the role of TFS in quality perception, 2) determine if the role of TFS in quality perception differs for listeners with normal hearing and listeners with hearing loss, and 3) quantify the relationship between intelligibility scores and quality ratings. Quality ratings were obtained using an 11-point scale for three signal processing types (two types of vocoding noise and total band removal), and with different amounts of background noise (none, 18, and 12 dB signal-to-noise ratios (SNRs)) for a range of frequency regions. TFS removal above 1500 Hz had a small, but measurable, effect on quality ratings for speech in quiet (i.e. a 2.2-point drop on an 11-point scale). For speech in noise, TFS removal had a smaller effect (at most a 1.2-point drop). TFS modifications also influenced the temporal envelope. Analyses using the Hearing Aid Speech Quality Index (HASQI) (Kates \u26 Arehart, 2010) showed that temporal envelope modifications provide a partial, though incomplete, description of sound quality degradation. Thus, TFS is important to consider in models of quality perception of speech. Intelligibility performance was correlated with quality ratings, with larger correlations evident for poorer intelligibility. However, a significant relationship between intelligibility and quality was documented even when intelligibility remained above 95%. The results of this study have both scientific and clinical implications. The findings provide insight into the mechanisms that affect sound quality perception, including the role of TFS. Additionally, this knowledge may be applied to future signal processing design, helping to maximize both speech intelligibility and sound quality in new hearing aids