Understanding hearing aid sound quality for music-listening

To improve speech intelligibility for individuals with hearing loss, hearing aids amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. Poor sound quality is also of concern for music-listening, in which intelligibility is likely not a consideration. A series of electroacoustic and behavioural studies were conducted to study sound quality issues in hearing aids, with a focus on music. An objective sound quality metric was validated for real hearing aid fittings, enabling researchers to predict sound quality impacts of signal processing adjustments. Qualitative interviews with hearing aid user musicians revealed that users’ primary concern was understanding the conductor’s speech during rehearsals, with hearing aid music sound quality issues a secondary concern. However, reported sound quality issues were consistent with music-listening sound quality complaints in the literature. Therefore, follow-up experiments focused on sound quality issues. An examination of different manufacturers’ hearing aids revealed significant music sound quality preferences for some devices over others. Electroacoustic measurements on these devices revealed that bass content varied more between devices than levels in other spectral ranges or nonlinearity, and increased bass levels were most associated with improved sound quality ratings. In a sound quality optimization study, listeners increased the bass and reduced the treble relative to typically-prescribed gains, for both speech and music. However, adjustments were smaller in magnitude for speech compared to music because they were also associated with a decline in speech intelligibility. These findings encourage the increase of bass and reduction of treble to improve hearing aid music sound quality, but only to the degree that speech intelligibility is not compromised. Future research is needed on the prediction of hearing aid music quality, the provision of low-frequency gain in open-fit hearing aids, genre-specific adjustments, hearing aid compression and music, and direct-to-consumer technology

Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

Accurate speech quality measures are highly attractive and beneficial in the design, fine-tuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

Modelling the perception of percussive low frequency instruments in rooms

Throughout the study of room acoustics, adverse effects and methods of controllingand mitigating modal behaviour are a well researched topic. However, despite this, agap between objective metrics and subjective results is still prevalent, thus resultingin a limited understanding of perceived bass quality.Previous work has suggested a group of perceptual attributes that are useful indescribing the effect of room acoustics on the perceived bass quality, however anobjective link to the perceptual attributes has not been quantified. Furthermore,the scope of previous work is mostly concerned with small listening rooms and rarelyextends to other cases, such as that found in live sound reinforcement.Hence, this work is focused on broadening the understanding of low frequencyquality due to modal behaviour in rooms, through extending the scope of research toinclude larger listening environments and single instrument excitation of the room.To investigate the characteristics of low frequency quality, various kick drumswere auralised using an improvement to the modal decomposition model and werethen rated in a subjective listening test using the descriptive bass quality attributes.From the results, the attributes were modelled through a novel approach using aRandom Forest model, utilising a combination of acoustic and MIR features.It was found that the perceptual attributes of both Resonance and Articulationwere predicted effectively from signal features, however Bass Energy was unable tobe modelled with any accuracy. Use of feature selection algorithms revealed thatResonance and Articulation attributes relied on temporal and decay based features,such as early decay time and temporal centroid. This result further suggests theimportance of temporal modal behaviour when considering audible effects due tolow frequency modes. The outcome of this work supports the growing body of workthat the effects of modal density are not as important as traditionally thought andis therefore applicable to both small and large rooms