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Supervised learning methods are a powerful tool for direction of arrival (DOA) estimation because they can cope with adverse conditions where simplified models fail. In this work, we consider a previously proposed convolutional neural network (CNN) approach that estimates the DOAs for multiple sources from the phase spectra of the microphones. For speech, specifically, the approach was shown to work well even when trained entirely on synthetically generated data. However, as each frame is processed separately, temporal context cannot be taken into account.


Signal synchronization in wireless acoustic sensor networks requires an accurate estimation of the sampling rate offset (SRO) inevitably present in signals acquired by sensors of ad-hoc networks. Although some sophisticated methods for blind SRO estimation have been recently proposed in this very young field of research, there is still a need for the development of new ideas and concepts especially regarding robust approaches with low computational complexity.


Distributed hardware of acoustic sensor networks bears inconsistency of local sampling frequencies, which is detrimental to signal processing. Fundamentally, sampling rate offset (SRO) nonlinearly relates the discrete-time signals acquired by different sensor nodes. As such, retrieval of SRO from the available signals requires nonlinear estimation, like double-cross-correlation processing (DXCP), and frequently results in biased estimation. SRO compensation by asynchronous sampling rate conversion (ASRC) on the signals then leaves an unacceptable residual.


Microphone array calibration is required to accurately capture the information in an audio source recording. Existing calibration methods require expensive hardware and setup procedures to compute filters for correcting microphone responses. Typically, such methods struggle to extend measurement accuracy to low frequencies. As a result, the error due to microphone gain mismatch propagates to all the modes in the spherical harmonic domain representation of a signal.


This paper considers the problem of estimating K angle of arrivals (AoA) using an array of M > K microphones. We assume the source signal is human voice, hence unknown to the receiver. Moreover, the signal components that arrive over K spatial paths are strongly correlated since they are delayed copies of the same source signal. Past works have successfully extracted the AoA of the direct path, or have assumed specific types of signals/channels to derive the subsequent (multipath) AoAs.