Küçük Abdullah, Panahi Issa M S
The University of Texas at Dallas Department of Electrical and Computer Engineering, 800 West Campbell Richardson, TX 75080, USA.
IEEE Int Workshop Mach Learn Signal Process. 2020 Sep;2020. doi: 10.1109/mlsp49062.2020.9231693. Epub 2020 Oct 20.
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.
这项工作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，该方法在安卓智能手机上实现，用于助听器应用。所提出的应用程序在安卓智能手机屏幕上提供说话者方向的“可视化”指示，以改善听力障碍者的听力。我们使用短时傅里叶变换（STFT）的实部和虚部作为所提出的用于DOA角度估计的CRNN架构的特征集。利用真实的智能手机录音来评估所提出方法的性能。在未见过（未训练）的环境中，所提出方法的准确率达到87.33%。这项工作还展示了所提出方法的实时推理，这仅使用安卓智能手机的两个内置麦克风，无需额外组件或外部硬件即可在安卓智能手机上完成。实时实现也证明了所提出的基于CRNN的模型的通用性和鲁棒性。
