Non-Intrusive Audio Quality Assessment Based on Deep Neural Network for Subjective MOS Prediction

Non-intrusive audio quality assessment, particularly for subjective MOS prediction for music signal, is crucial in realtime audio communication and playback systems. While network-based methods have been extensively used for objective speech quality assessment, evaluating audio quality presents a greater challenge due to higher sampling rates and more complex signal spectrum. In this paper, we design a non-intrusive audio quality assessment system based on deep neural network for subjective MOS prediction of distorted audio signals. Mixed perceptual features are extracted for signal analysis, and both objective and subjective indicators are utilized as labels for two-step training on simulated data. Besides, we apply improved convolution layers, attention layers, and a type of new loss to improve the performance of our model. The experimental results show that the proposed system performs better than conventional assessment methods in correlation.