PACDNN: A phase-aware composite deep neural network for speech enhancement.

Most of the current approaches for speech enhancement (SE) using deep neural network (DNN) face a number of limitations: they do not exploit information contained in the phase spectrum while their high computational complexity and memory requirements make them unsuited for real-time applications. In this paper, a new phase-aware composite deep neural network (PACDNN) is introduced to address these challenges. Specifically, magnitude processing with spectral mask and phase reconstruction with phase derivative are proposed as key subtasks of the new network to simultaneously enhance the magnitude and phase spectra. Besides, the DNN is meticulously designed to take advantage of strong temporal and spectral dependencies of speech, while its components perform independently and in parallel to speed up the computation. The advantages of the proposed PACDNN model over some well-known DNN-based SE methods are demonstrated through extensive comparative experiments. • A novel phase-aware composite DNN for speech magnitude and phase enhancement. • Exploiting improved LSTM and CNN to extract a complementary set of features. • Parallel streams to speed up the computations. • Low complexity in terms of the number of trainable parameters and memory. • Extensive comparative experiments showing a competitive performance. [ABSTRACT FROM AUTHOR]