CONVOLUTIONAL RECURRENT NEURAL NETWORK BASED DIRECTION OF ARRIVAL ESTIMATION METHOD USING TWO MICROPHONES FOR HEARING STUDIES.

Abdullah Küçük, Issa M S Panahi
{"title":"CONVOLUTIONAL RECURRENT NEURAL NETWORK BASED DIRECTION OF ARRIVAL ESTIMATION METHOD USING TWO MICROPHONES FOR HEARING STUDIES.","authors":"Abdullah Küçük,&nbsp;Issa M S Panahi","doi":"10.1109/mlsp49062.2020.9231693","DOIUrl":null,"url":null,"abstract":"<p><p>This work proposes a convolutional recurrent neural network (CRNN) based direction of arrival (DOA) angle estimation method, implemented on the Android smartphone for hearing aid applications. The proposed app 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. We use real and imaginary parts of short-time Fourier transform (STFT) as a feature set for the proposed CRNN architecture for DOA angle estimation. Real smartphone recordings are utilized for assessing performance of the proposed method. The accuracy of the proposed method reaches 87.33% for unseen (untrained) environments. This work also presents real-time inference of the proposed method, which is done on an Android smartphone using only its two built-in microphones and no additional component or external hardware. The real-time implementation also proves the generalization and robustness of the proposed CRNN based model.</p>","PeriodicalId":73290,"journal":{"name":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","volume":"2020 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/mlsp49062.2020.9231693","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/mlsp49062.2020.9231693","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/10/20 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This work proposes a convolutional recurrent neural network (CRNN) based direction of arrival (DOA) angle estimation method, implemented on the Android smartphone for hearing aid applications. The proposed app 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. We use real and imaginary parts of short-time Fourier transform (STFT) as a feature set for the proposed CRNN architecture for DOA angle estimation. Real smartphone recordings are utilized for assessing performance of the proposed method. The accuracy of the proposed method reaches 87.33% for unseen (untrained) environments. This work also presents real-time inference of the proposed method, which is done on an Android smartphone using only its two built-in microphones and no additional component or external hardware. The real-time implementation also proves the generalization and robustness of the proposed CRNN based model.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于卷积递归神经网络的双传声器听觉到达方向估计方法研究。
本文提出了一种基于卷积递归神经网络(CRNN)的到达方向(DOA)角度估计方法,并在助听器应用的Android智能手机上实现。这款应用程序可以在安卓智能手机的屏幕上为说话者提供“视觉”指示,以改善听力障碍患者的听力。我们使用短时傅里叶变换(STFT)的实部和虚部作为所提出的CRNN体系结构的特征集,用于DOA角度估计。真实的智能手机录音被用于评估所提出的方法的性能。对于未见过的(未经训练的)环境,该方法的准确率达到87.33%。这项工作还提出了所提出方法的实时推断,该方法在Android智能手机上完成,仅使用其两个内置麦克风,没有额外的组件或外部硬件。实时性验证了该模型的泛化性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
DATA-DRIVEN LEARNING OF GEOMETRIC SCATTERING MODULES FOR GNNS. CONVOLUTIONAL RECURRENT NEURAL NETWORK BASED DIRECTION OF ARRIVAL ESTIMATION METHOD USING TWO MICROPHONES FOR HEARING STUDIES. LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS. Statistical modelling and inference Probabilistic graphical models
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1