Deep Complex-Valued Convolutional-Recurrent Networks for Single Source DOA Estimation

Abstract

Despite having conceptual and practical advantages, Complex-Valued Neural Networkss (CVNNs) have been much less explored for audio signal processing tasks than their real-valued counterparts. We investigate the use of a complex-valued Convolutional Recurrent Neural Network (CRNN) for Direction-of-Arrival (DOA) estimation of a single sound source on an enclosed room. By training and testing our model with recordings from the DCASE 2019 dataset, we show our architecture compares favourably to a real-valued CRNN counterpart both in terms of estimation error as well as speed of convergence. We also show visualizations of the complex-valued feature representations learned by our method and provide interpretations for them.

The Neural-SRP Method for Universal Robust Multi-source Tracking

“…Finally, networks can be classified according to the input feature used, such as the complex-valued multichannel STFT [27], its phase [24], or the GCC-PHAT between all microphone pairs [13], [25]. If the input feature consists of the output of a classical signal processing method, such as the SRP maps shown in Fig.…”