Blind DOA Estimation in a Reverberant Environment Based on Hybrid Initialized Multichannel Deep 2-D Convolutional NMF With Feedback Mechanism

Abstract

The accuracy performance of traditional direction of arrival (DOA) estimation algorithms is seriously affected by the reverberation. Considering the advantage of the sparse characteristic of speech signal in time-frequency (T-F) domain, this paper presents a new blind DOA estimation method based on integrated deep learning and convolutional non-negative matrix factorization (NMF). Firstly, mathematic models of microphone array and room impulse response are built. In addition, we extracted blindly initialization parameters of 2-D convolutional NMF using k-means clustering algorithm and singular value decomposition algorithm, which can be used to accurately estimate the main components of desired sound source in the reverberation environment of multi-path propagation. Moreover, the feedback mechanism is introduced into deep 2-D convolutional NMF and correlation coefficient between the signal decomposed by NMF and the signal to be decomposed is used to select the best separated signal for DOA estimation, which make the separation algorithm simpler and more efficient. Finally, test of orthogonality of projected subspaces (TOPS) algorithm is used to validate the DOA estimation capability of this algorithm. Compared with the unprocessed reverberation speech, the estimation error is reduced, which shows that the proposed algorithm can effectively improve the estimation accuracy of DOA estimation when the received signals are in a reverberant environment.

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

“…As shown in Table 6, increasing the SNR from −5 dB to 5 dB in [51] reduces its RMSE from 0.08 to 0.02. Increasing the SNR from −5 dB to 5 dB in [53] reduces its RMSE from 3.3 to 0.3, while the RMSE drops from 1.3 to 0.28 when increasing the SNR from −10 dB to 10 dB in [43]. From the above relationship between SNR and RMSE, it can be seen that as the signal's SNR increases, the DOA estimation performance of the algorithm also improves.…”

“…Training and testing were performed using data sets of simulated and real environments, respectively, and performance evaluation showed that DOA estimation was improved even in noisy and reverberant environments. Fu et al [53] proposed a new blind DOA estimation method that uses the 2D convolution nonnegative matrix factorization method to generate a new array signal to estimate the azimuth angle of the reverberation signal. Wajid et al [55] proposed to use the recurrent neural network (RNN) model to learn some similar features used in DAS beamforming.…”

“…By comparing each method, Fu et al [53] had the best performance when using RMSE as the evaluation method, and RMSE is 0. When using A as the evaluation method, Yang et al [50] had the best performance, and A is 100%.…”

“…e most commonly used evaluation criteria are rootmean-squared error (RMSE), accuracy (A), and mean absolute error (MAE), accounting for 42%, 27%, and 16%, respectively. Fu et al [53] had the best performance when using RMSE as the evaluation method, and RMSE is 0. When using A as the evaluation method, Yang et al [50] had the best performance, and A is 100%.…”

Blind Source Separation Algorithm for Chaotic Masking Multipath Signals Based on Spectral Peak Search Counter Permutation

“…Two methods exists to solve the uncertainty problem of frequency-domain method permutation: the first method is the geometric method based on the direction of arrival (DOA) [15]- [17], and the second method is the mutual parameter method that is based on the correlation between adjacent frequency points [18]- [21]. The DOA-based method considers the source signal and the spatial position of the sensor as prior knowledge.…”