Deep learning-based direction-of-arrival estimation for multiple speech sources using a small scale array

Abstract

A high resolution direction-of-arrival (DOA) approach is presented based on deep neural networks (DNNs) for multiple speech sources localization using a small scale array. First, three invariant features from the time-frequency spectrum of the input signal include generalized cross correlation (GCC) coefficients, GCC coefficients in the mel-scaled subband, and the combination of GCC coefficients and logarithmic mel spectrogram. Then the DNN labels are designed to fit the Gaussian distribution, which is similar to the spatial spectrum of the multiple signal classification. Finally, DOAs are predicted by performing peak detection on the DNN outputs, where the maximum values correspond to speech signals of interest. The DNN-based DOA estimation method outperforms the existing high resolution beamforming techniques in numerical simulations. The proposed framework implemented with a four-element microphone array can effectively localize multiple speech sources in an indoor environment.

Performance evaluation of direction-finding techniques of an acoustic source with uniform linear array

“…MUSIC algorithm is a subspace algorithm that uses data collected from ULA to estimate covariance matrix to form subspaces. The steering vector is imposed on the noise-only subspace which leads to the formation of the pseudo-spectrum, the number of peaks in the pseudo-spectrum represents the number of sources and the angular value at which peaks occur is the estimated DOA (Zhang et al , 2021). The eigen-decomposition is used to separate noise subspace and signal subspace.…”

“…Digital signal processors have been used as an approach for finding the direction. Methods such as subspace decomposition, analysis of eigen values and compressed sensing-based methods are playing an important role in achieving better performance in terms of speed, accuracy and robustness (Ge et al , 2021; Zhang et al , 2021).…”

“…Based on findings, it is being believed that advanced technologies like deep learning has improved the direction-finding techniques to a greater extent. Such kind of study helps researchers to conduct detailed analysis (Ge et al , 2021; Zhang et al , 2021).…”

Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array

“…In recent years, DOA estimation methods based on deep learning [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] have become another research hotspot in the direction-finding field. This kind of method does not need to build a parameter model, but directly learns the nonlinear relationship between the array output and the incoming wave direction from the data set, so as to realize DOA estimation.…”