ArticleThe Journal of the Acoustical Society of America2020
Efficient two-microphone speech enhancement using basic recurrent neural network cell for hearing and hearing aids.
Article in The Journal of the Acoustical Society of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Exploring the health literacy status of people with hearing impairment: a systematic review.Archives of public health = Archives belges de sante publique · 2023Pooled it
- Smartphone-based single-channel speech enhancement application for hearing aids.The Journal of the Acoustical Society of America · 2021Article
- Real-time single-channel deep neural network-based speech enhancement on edge devices.Interspeech · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
This work presents a two-microphone speech enhancement (SE) framework based on basic recurrent neural network (RNN) cell. The proposed method operates in real-time, improving the speech quality and intelligibility in noisy environments. The RNN model trained using a simple feature set-real and imaginary parts of the short-time Fourier transform (STFT) are computationally efficient with a minimal input-output processing delay. The proposed algorithm can be used in any stand-alone platform such as a smartphone using its two inbuilt microphones. The detailed operation of the real-time implementation on the smartphone is presented. The developed application works as an assistive tool for hearing aid devices (HADs). Speech quality and intelligibility test results are used to compare the proposed algorithm to existing conventional and neural network-based SE methods. Subjective and objective scores show the superior performance of the developed method over several conventional methods in different noise conditions and low signal to noise ratios (SNRs).
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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.