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Conference Paper
Development of a Psychoacoustic Loss Function for the Deep Neural Network (DNN)-Based Speech Coder
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- Authors
- Joon Byun, Seungmin Shin, Youngcheol Park, Jongmo Sung, Seungkwon Beack
- Issue Date
- 2021-09
- Citation
- International Speech Communication Association (INTERSPEECH) 2021, pp.1694-1698
- Publisher
- ISCA
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.