ArticleIEEE access : practical innovations, open solutions2019
Real-time Convolutional Neural Network based Speech Source Localization on 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 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- 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
- 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
- Spectral Flux-Based Convolutional Neural Network Architecture for Speech Source Localization and Its Real-Time Implementation.IEEE access : practical innovations, open solutions · 2020Article
- Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications.IEEE access : practical innovations, open solutions · 2020Article
- Direction of arrival estimation using deep neural network for hearing aid applications using 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
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
In this paper, we present a real-time convolutional neural network (CNN) based approach for speech source localization (SSL) using Android-based smartphone and its two built-in microphones under noisy conditions. We propose a new input feature set - using real and imaginary parts of the short-time Fourier transform (STFT) for CNN-based SSL. We use simulated noisy data from popular datasets that was augmented with few hours of real recordings collected on smartphones to train our CNN model. We compare the proposed method to recent CNN-based SSL methods that are trained on our dataset and show that our CNN-based SSL method offers higher accuracy on identical test datasets. Another unique aspect of this work is that we perform real-time inferencing of our CNN model on an Android smartphone with low latency (14 milliseconds(ms) for single frame-based estimation, 180 ms for multi frame-based estimation and frame length is 20 ms for both cases) and high accuracy (i.e. 88.83% at 0dB SNR). We show that our CNN model is rather robust to smartphone hardware mismatch, hence we may not need to retrain the entire model again for use with different smartphones. The proposed application provides a 'visual' indication of the direction of a talker on the screen of Android smartphones for improving the hearing of people with hearing disorders.
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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.