Efficacy of Directional Microphones in Hearing Aids Equipped with Wireless Synchronization Technology

Geetha, Chinnaraj; Tanniru, Kishore; Rajan, R Raja

doi:10.5152/iao.2017.2820

Cited by 7 publications

(10 citation statements)

References 11 publications

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“…Mathai and Mohammed [30] assessed the effect of compression release time in hearing aids and presentation levels on sentence perception in the presence of noise using the SNR-50 task on normal hearing and hearing-impaired elderly individuals. Geetha, Tanniru, and Rajan [31] evaluated the benefit of directionality in wireless hearing aids in individuals with hearing impairment. In their study, the speech perception in noise was assessed using the SNR-50 task with the sentences developed by Geetha et al [15].…”

Section: Applicationsmentioning

confidence: 99%

Assessment of the Clinical Utility of the Sentence Identification Test in Kannada for Adults

Geetha¹,

Kumar²,

Manjula³

et al. 2021

J Hear Sci

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Introduction: A sentence identification test was developed by Geetha, Kumar, Manjula, and Pavan (2014) in the Kannada language. The test consists of 25 equivalent sentence lists with 10 sentences each. The present study aimed to assess the clinical utility of this test.Material and methods: All sentences were presented to 5 groups of adults with 10 individuals in each group. Four groups, each of 10 individuals, had hearing loss: mild, moderate, moderately-severe, or severe. The fifth group had 40 individuals with normal hearing sensitivity. Standardized lists of 25 sentences were presented monaurally at the most comfortable level in a sound-treated double room. The number of correctly identified words was tabulated. Results:The mean identification scores decreased with increase in the degree of hearing loss, although the scores were comparable between the normal and mild group. A comparison of scores between each list within each group revealed that there was no significant difference between the lists for the scores obtained from the individuals with mild, moderate, moderately-severe, and severe degrees of hearing loss. Conclusions:The developed sentence material is sensitive to differences in speech identification ability across different degrees of hearing loss. In addition, the mean number of correctly identified words do not vary across the lists in any of the four groups, suggesting equivalency across the standardized 25 lists in the clinical groups.

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Section: Applicationsmentioning

confidence: 99%

Assessment of the Clinical Utility of the Sentence Identification Test in Kannada for Adults

Geetha¹,

Kumar²,

Manjula³

et al. 2021

J Hear Sci

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“…Note that the proposed methods start from binaural signal features, which implies that binaural rather than bilateral hearing aids are required when using these methods for sound source localization in hearing aid systems, and the issue of binaural hearing aids that need to transmit and synchronize the binaural features needed for this model is beyond the scope of this paper. Yet there is a large body of research literature that addresses this issue, and the reader is referred to Kreisman et al ( 2010 ), Ibrahim et al ( 2013 ), Wei et al ( 2014 ), and Geetha et al ( 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Toward learning robust contrastive embeddings for binaural sound source localization

Tang

Taseska

Waterschoot

2022

Front. Neuroinform.

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Recent deep neural network based methods provide accurate binaural source localization performance. These data-driven models map measured binaural cues directly to source locations hence their performance highly depend on the training data distribution. In this paper, we propose a parametric embedding that maps the binaural cues to a low-dimensional space where localization can be done with a nearest-neighbor regression. We implement the embedding using a neural network, optimized to map points that are close to each other in the latent space (the space of source azimuths or elevations) to nearby points in the embedding space, thus the Euclidean distances between the embeddings reflect their source proximities, and the structure of the embeddings forms a manifold, which provides interpretability to the embeddings. We show that the proposed embedding generalizes well in various acoustic conditions (with reverberation) different from those encountered during training, and provides better performance than unsupervised embeddings previously used for binaural localization. In addition, the proposed method performs better than or equally well as a feed-forward neural network based model that directly estimates the source locations from the binaural cues, and it has better results than the feed-forward model when a small amount of training data is used. Moreover, we also compare the proposed embedding using both supervised and weakly supervised learning, and show that in both conditions, the resulting embeddings perform similarly well, but the weakly supervised embedding allows to estimate source azimuth and elevation simultaneously.

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“…For example, they increase speech perception and comfort in background noise while also decreasing the effort required to listen [1]. The features include, but are not limited to, directional microphones [2][3][4][5][6][7][8][9][10] noise reduction algorithms [11], dynamic range compression [12] and proper fitting and verification [13][14][15][16]. What is notable is that each of these factors was conceptualized, tested, validated and incorporated into current hearing aid devices via digital signal processing (DSP) techniques, advanced hearing aid technology, and more recently, machine learning approaches [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

On a Vector towards a Novel Hearing Aid Feature: What Can We Learn from Modern Family, Voice Classification and Deep Learning Algorithms

et al. 2021

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(1) Background: The application of machine learning techniques in the speech recognition literature has become a large field of study. Here, we aim to (1) expand the available evidence for the use of machine learning techniques for voice classification and (2) discuss the implications of such approaches towards the development of novel hearing aid features (i.e., voice familiarity detection). To do this, we built and tested a Convolutional Neural Network (CNN) Model for the identification and classification of a series of voices, namely the 10 cast members of the popular television show “Modern Family”. (2) Methods: Representative voice samples were selected from Season 1 of Modern Family (N = 300; 30 samples for each of the classes of the classification in this model, namely Phil, Claire, Hailey, Alex, Luke, Gloria, Jay, Manny, Mitch, Cameron). The audio samples were then cleaned and normalized. Feature extraction was then implemented and used as the input to train a basic CNN model and an advanced CNN model. (3) Results: Accuracy of voice classification for the basic model was 89%. Accuracy of the voice classification for the advanced model was 99%.; (4) Conclusions: Greater familiarity with a voice is known to be beneficial for speech recognition. If a hearing aid can eventually be programmed to recognize voices that are familiar or not, perhaps it can also apply familiar voice features to improve hearing performance. Here we discuss how such machine learning, when applied to voice recognition, is a potential technological solution in the coming years.

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Efficacy of Directional Microphones in Hearing Aids Equipped with Wireless Synchronization Technology

Cited by 7 publications

References 11 publications

Assessment of the Clinical Utility of the Sentence Identification Test in Kannada for Adults

Assessment of the Clinical Utility of the Sentence Identification Test in Kannada for Adults

Toward learning robust contrastive embeddings for binaural sound source localization

On a Vector towards a Novel Hearing Aid Feature: What Can We Learn from Modern Family, Voice Classification and Deep Learning Algorithms

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