Monaural Source Separation Based on Adaptive Discriminative Criterion in Neural Networks

Monaural source separation is an important research area which can help to improve the performance of several real-world applications, such as speech recognition and assisted living systems. Huang et al. proposed deep recurrent neural networks (DRNNs) with discriminative criterion objective function to improve the performance of source separation. However, the penalty factor in the objective function is selected randomly and empirically. Therefore, we introduce an approach to calculate the parameter in the discriminative term adaptively via the discrepancy between target features. The penalty factor can be changed with inputs to improve the separation performance. The proposed method is evaluated with different settings and architectures of neural networks. In these experiments, the TIMIT corpus is explored as the database and the signal to distortion ratio (SDR) as the measurement. Comparing with the previous approach, our method has improved robustness and a better separation performance.