ArticleIEEE access : practical innovations, open solutions2019
A Real-Time Convolutional Neural Network Based Speech Enhancement for Hearing Impaired Listeners Using Smartphone.
Article in IEEE access : practical innovations, open solutions, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 67 citations in OpenAlex.
- Smartphone-based single-channel speech enhancement application for hearing aids.The Journal of the Acoustical Society of America · 2021Article
- Development and Pilot Testing of Smartphone-Based Hearing Test Application.International journal of environmental research and public health · 2021Article
- Robust Three-Microphone Speech Source Localization Using Randomized Singular Value Decomposition.IEEE access : practical innovations, open solutions · 2021Article
- Behavioral Validation of the Smartphone for Remote Microphone Technology.Seminars in hearing · 2020Review
- Real-time single-channel deep neural network-based speech enhancement on edge devices.Interspeech · 2020Article
- CONVOLUTIONAL RECURRENT NEURAL NETWORK BASED DIRECTION OF ARRIVAL ESTIMATION METHOD USING TWO MICROPHONES FOR HEARING STUDIES.IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing · 2020Article
- Automated machine learning based speech classification for hearing aid applications and its real-time implementation on smartphone.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Efficient two-microphone speech enhancement using basic recurrent neural network cell for hearing and hearing aids.The Journal of the Acoustical Society of America · 2020Article
- Real-time dual-channel speech enhancement by VAD assisted MVDR beamformer for hearing aid applications using smartphone.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications.IEEE access : practical innovations, open solutions · 2020Article
- A Computationally efficient IVA-based Blind Source Separation for Hearing Aid Applications and its Real-time Implementation on Smartphone.Proceedings of meetings on acoustics. Acoustical Society of America · 2019Article
- Frequency-based multi-band adaptive compression for hearing aid application.Proceedings of meetings on acoustics. Acoustical Society of America · 2019Article
- Real-time Convolutional Neural Network based Speech Source Localization on Smartphone.IEEE access : practical innovations, open solutions · 2019Article
- A Hearing Test Simulator GUI for clinical testing of speech recognition.Proceedings of meetings on acoustics. Acoustical Society of America · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
This paper presents a Speech Enhancement (SE) technique based on multi-objective learning convolutional neural network to improve the overall quality of speech perceived by Hearing Aid (HA) users. The proposed method is implemented on a smartphone as an application that performs real-time SE. This arrangement works as an assistive tool to HA. A multi-objective learning architecture including primary and secondary features uses a mapping-based convolutional neural network (CNN) model to remove noise from a noisy speech spectrum. The algorithm is computationally fast and has a low processing delay which enables it to operate seamlessly on a smartphone. The steps and the detailed analysis of real-time implementation are discussed. The proposed method is compared with existing conventional and neural network-based SE techniques through speech quality and intelligibility metrics in various noisy speech conditions. The key contribution of this paper includes the realization of CNN SE model on a smartphone processor that works seamlessly with HA. The experimental results demonstrate significant improvements over the state-of-the-art techniques and reflect the usability of the developed SE application in noisy environments.
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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.