Development of a Psychoacoustic Loss Function for the Deep Neural Network (DNN)-Based Speech Coder
(3 minutes introduction)
|Joon Byun (Yonsei University, Korea), Seungmin Shin (Yonsei University, Korea), Youngcheol Park (Yonsei University, Korea), Jongmo Sung (ETRI, Korea), Seungkwon Beack (ETRI, Korea)|
This paper presents a loss function to compensate for the perceptual loss of the deep neural network (DNN)-based speech coder. By utilizing the psychoacoustic model (PAM), we design a loss function to maximize the mask-to-noise ratio (MNR) in multi-resolution Mel-frequency scales. Also, a perceptual entropy (PE)-based weighting scheme is incorporated onto the MNR loss so that the DNN model focuses more on perceptually important Mel-frequency bands. The proposed loss function was tested on a CNN-based autoencoder implementing the softmax quantization and entropy-based bitrate control. Objective and subjective tests conducted with speech signals showed that the proposed loss function produced higher perceptual quality than the previous perceptual loss functions.