Comparative Analysis of Neural Networks for Noise Suppression in Audio Signals

The article explores in detail the use of neural networks in order of solve the problem of noise reduction in audio signals, representing the one of the critical problems of modern audio processing technologies. The methods and algorithms aimed at improving sound quality by eliminating background noise, which significantly reduce the intelligibility of audio signals and negatively affect their perception, are analyzed. Compared to traditional approaches such as filtering or adaptive algorithms, neural networks demonstrate higher efficiency due to their powerful data processing tools. Special attention is paid to various neural network architectures, including convolutional networks, recurrent networks and their combinations. These models consider both temporal and spectral characteristics of audio signals, which allows to reach more accurate noise reduction. Objective metrics, such as the signal-to-noise ratio, as well as subjective indicators, such as the quality of sound perception by listeners, were used to evaluate the experimental results. Experiments have prooved that the approaches proposed are significantly superior to traditional methods, providing a visible improvement in the quality of audio signals. The study demonstrates perspective of using neural networks for noise reduction, opening up new possibilities for application in communication systems, multimedia, audio technologies and other areas requiring high-quality sound processing.