Construction of Noise Reduction and Sound Quality Optimization Model for Music Transmission Signals Based on Deep Learning

ABSTRACT This study takes the noise interference and sound quality attenuation problems of music signals during transmission as the core research, and constructs a noise reduction and sound quality optimization model for music transmission signals based on deep learning. The study collects typical music samples and multiple types of noise data, preprocesses the signals using time‐frequency domain feature extraction methods, and on this basis, designs a deep neural network structure that integrates convolutional neural networks and perceptual optimization mechanisms. During the model training process, adaptive learning rates and perceptual loss functions are introduced to enhance the convergence speed and auditory consistency of the network in different noise environments. The experimental results show that this model outperforms the traditional spectral subtraction method, Wiener filtering, and ordinary CNN models in multiple indicators. Among them, the objective evaluation values such as PESQ, SDR, and STOI have significantly improved, the MOS subjective listening experience score reaches 3.87, and the naturalness of sound quality and the degree of detail restoration have been significantly improved. The model demonstrates excellent generalization ability and robustness under various noise types, effectively reducing background noise while maintaining the original timbre characteristics. The research results show that the combination of deep learning and perceptual optimization provides a new solution for noise reduction and sound quality enhancement of music signals, and can be widely applied in fields such as music production, audio restoration, and intelligent voice transmission. Future research will explore lightweight models and cross‐modal optimization methods to achieve real‐time high‐fidelity audio processing.