1,720,980 research outputs found
Dataset for Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners
No full textDataset supporting 'Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners' published in the Journal of the Acoustical Society of America.
Dataset showing the raw (anonymized) data of intelligibility, quality and audiograms. These data allow complete reconstruction of figures in paper
Dataset DOI assigned 10.5258/SOTON/D0020</span
Dataset for Sensitivity to Envelope ITDs at High Modulation Rates
No full textData for Monaghan, Bleeck, McAlpine, "Sensitivity to Envelope ITDs at High Modulation Rates", Trends in Hearing.
Shown are thresholds for all (anonymous) participants for various frequencies for all conditions as described in the paper.</span
Tolerable delay for speech production and perception: effects of hearing ability and experience with hearing aids
No full textObjective: processing delay is one of the important factors that limit the development of novel algorithms for hearing devices. In this study, both normal-hearing listeners and listeners with hearing loss were tested for their tolerance of processing delay up to 50 ms using a real-time setup for own-voice and external-voice conditions based on linear processing to avoid confounding effects of time-dependent gain. Design: participants rated their perceived subjective annoyance for each condition on a 7-point Likert scale. Study sample: Twenty normal-hearing participants and twenty participants with a range of mild to moderate hearing losses. Results: delay tolerance was significantly greater for the participants with hearing loss in two out of three voice conditions. The average slopes of annoyance ratings were negatively correlated with the degree of hearing loss across participants. A small trend of higher tolerance of delay by experienced users of hearing aids in comparison to new users was not significant. Conclusion: the increased tolerance of processing delay for speech production and perception with hearing loss and reduced sensitivity to changes in delay with stronger hearing loss may be beneficial for novel algorithms for hearing devices but the setup used in this study differed from commercial hearing aids
Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users
No full textSpeech understanding in noisy environments is still one of the major challenges for cochlear implant(CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks(NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices
Sensitivity to envelope interaural time differences at high modulation rates
No full textSensitivity to interaural time differences (ITDs) conveyed in the temporal fine structure of low-frequency tones and the modulated envelopes of high-frequency sounds are considered comparable, particularly for envelopes shaped to transmit similar fidelity of temporal information normally present for low-frequency sounds. Nevertheless, discrimination performance for envelope modulation rates above a few hundred Hertz is reported to be poor—to the point of discrimination thresholds being unattainable—compared with the much higher (>1,000?Hz) limit for low-frequency ITD sensitivity, suggesting the presence of a low-pass filter in the envelope domain. Further, performance for identical modulation rates appears to decline with increasing carrier frequency, supporting the view that the low-pass characteristics observed for envelope ITD processing is carrier-frequency dependent. Here, we assessed listeners’ sensitivity to ITDs conveyed in pure tones and in the modulated envelopes of high-frequency tones. ITD discrimination for the modulated high-frequency tones was measured as a function of both modulation rate and carrier frequency. Some well-trained listeners appear able to discriminate ITDs extremely well, even at modulation rates well beyond 500?Hz, for 4-kHz carriers. For one listener, thresholds were even obtained for a modulation rate of 800?Hz. The highest modulation rate for which thresholds could be obtained declined with increasing carrier frequency for all listeners. At 10?kHz, the highest modulation rate at which thresholds could be obtained was 600?Hz. The upper limit of sensitivity to ITDs conveyed in the envelope of high-frequency modulated sounds appears to be higher than previously considered
Speech enhancement for hearing-impaired listeners using deep neural networks with auditory-model based features
No full textSpeech understanding in adverse acoustic environments is still a major problem for users of hearing-instruments. Recent studies on supervised speech segregation show good promise to alleviate this problem by separating speech-dominated from noise-dominated spectro-temporal regions with estimated time-frequency masks. The current study compared a previously proposed feature set to a novel auditory-model based feature set using a common deep neural network based speech enhancement framework. The performance of both feature extraction methods was evaluated with objective measurements and a subjective listening test to measure speech perception scores in terms of intelligibility and quality with 17 hearing-impaired listeners. Significant improvements in speech intelligibility and quality ratings were found for both feature extraction systems. However, the auditory-model based feature set showed superior performance compared to the comparison feature set indicating that auditory-model based processing could provide further improvements for supervised speech segregation systems and their potential applications in hearing instruments
Speech Enhancement Based on Neural Networks Improves Speech Intelligibility in Noise for Cochlear Implant Users
No full textThese files show the complete anonymized dataset of the results of psychophysical experiments of all conditions that led to the figures in the paper. All figures can be redrawn on the basis of these data.
Shown are the conditions in the columns and the different noise types in the rows. Results are SRTs in each condition</span
Speech enhancement based on artificial neural networks for hearing-impaired listeners using auditory inspired features
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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