Abstract
Traditional separation methods have limited ability to handle the speech separation problem in high reverberant and low signal-to-noise ratio (SNR) environments, and thus achieve unsatisfactory results. In this study, a convolutional neural network with temporal convolution and residual network (TC-ResNet) is proposed to realize speech separation in a complex acoustic environment. A simplified steered-response power phase transform, denoted as GSRP-PHAT, is employed to reduce the computational cost. The extracted features are reshaped to a special tensor as the system inputs and implements temporal convolution, which not only enlarges the receptive field of the convolution layer but also significantly reduces the network computational cost. Residual blocks are used to combine multiresolution features and accelerate the training procedure. A modified ideal ratio mask is applied as the training target. Simulation results demonstrate that the proposed microphone array speech separation algorithm based on TC-ResNet achieves a better performance in terms of distortion ratio, source-to-interference ratio, and short-time objective intelligibility in low SNR and high reverberant environments, particularly in untrained situations. This indicates that the proposed method has generalization to untrained conditions.
Highlights
Speech separation, as a front-end speech signal processing system, is widely applied in various scenarios, such as smart homes [1], hearing aids, and teleconferencing
The time difference of arrival (TDOA) is preferred [5], as it can be conveniently inferred by a generalized cross-correlation (GCC) function [6]
long short-term memory network (LSTM) still has some shortcomings in practical applications, such as insufficient training, lack of speaker robustness, and the need for an additional permutation invariant training (PIT) procedure to match the masks on a specific utterance-level speech
Summary
As a front-end speech signal processing system, is widely applied in various scenarios, such as smart homes [1], hearing aids, and teleconferencing. Prior to the success of deep learning methods, speech separation methods mainly depended on spectrum masks to obtain the target speech LSTM still has some shortcomings in practical applications, such as insufficient training, lack of speaker robustness, and the need for an additional permutation invariant training (PIT) procedure to match the masks on a specific utterance-level speech To address these problems, more advanced networks have been proposed. A fully convolutional time-domain audio separation network [11,12] is presented based on a temporal convolutional network [13,14], whose task is to obtain speaker-independent speech from a single-channel reverbed speech It modifies the long sequential model of RNN and efficiently implements the data training [15], but still requires a PIT procedure.
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