End-to-End Sound Source Enhancement Using Deep Neural Network in the Modified Discrete Cosine Transform Domain
Yuma Koizumi, Noboru Harada, Yoichi Haneda, Yusuke Hioka, Kazunori Kobayashi · 2018
This paper presents an end-to-end deep neural network (DNN)-based source enhancement on the basis of a time-frequency (T-F) mask processing in the modified discrete cosine transform (MDCT)-domain. To retrieve the target signal perfectly in the discrete Fourier transform (DFT)-domain, both amplitude and phase of the spectrum need to be manipulated. However, since it is difficult to deal with complex values by neural network straightforward way, a real-valued T-F mask is commonly estimated and only amplitude spectrum is manipulated. In this study, we use the MDCT instead of the DFT and estimate real-valued T-F masks in the MDCT-domain. The perfect retrieval can be achieved by manipulating only the real-valued MDCT-spectra. To reduce time-domain aliasing arises from manipulating the MDCT spectrum, we build an end-to-end DNN-based source enhancement using T-F mask and train the DNN to minimize an objective function defined in the time-domain. In experiments using several kinds of objective sound quality scores, we observed that the scores were significantly improved.