ArticleThe Journal of the Acoustical Society of America2021
An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners.
Article in The Journal of the Acoustical Society of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- The Application of Time-Frequency Masking To Improve Intelligibility of Dysarthric Speech in Background Noise.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Progress made in the efficacy and viability of deep-learning-based noise reduction.The Journal of the Acoustical Society of America · 2023Article
- Restoring speech intelligibility for hearing aid users with deep learning.Scientific reports · 2023Article
- A causal and talker-independent speaker separation/dereverberation deep learning algorithm: Cost associated with conversion to real-time capable operation.The Journal of the Acoustical Society of America · 2021Article
- Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.The Journal of the Acoustical Society of America · 2021Article
- Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods.Trends in hearingReview
- Objective Evaluation of a Deep Learning-Based Noise Reduction Algorithm for Hearing Aids Under Diverse Fitting and Listening Conditions.Trends in hearingArticle
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Real-time operation is critical for noise reduction in hearing technology. The essential requirement of real-time operation is causality-that an algorithm does not use future time-frame information and, instead, completes its operation by the end of the current time frame. This requirement is extended currently through the concept of "effectively causal," in which future time-frame information within the brief delay tolerance of the human speech-perception mechanism is used. Effectively causal deep learning was used to separate speech from background noise and improve intelligibility for hearing-impaired listeners. A single-microphone, gated convolutional recurrent network was used to perform complex spectral mapping. By estimating both the real and imaginary parts of the noise-free speech, both the magnitude and phase of the estimated noise-free speech were obtained. The deep neural network was trained using a large set of noises and tested using complex noises not employed during training. Significant algorithm benefit was observed in every condition, which was largest for those with the greatest hearing loss. Allowable delays across different communication settings are reviewed and assessed. The current work demonstrates that effectively causal deep learning can significantly improve intelligibility for one of the largest populations of need in challenging conditions involving untrained background noises.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.