Music Enhancement

Music enhancement aims to improve the quality of audio recordings by removing noise, reverberation, and other artifacts, ultimately enhancing the listening experience. Current research focuses on developing robust deep learning models, including neural networks, transformers, and generative adversarial networks (GANs), often leveraging techniques like beamforming and mel-spectrogram manipulation for single and multi-channel audio processing. These advancements are driven by the increasing availability of large datasets and the need for efficient, real-time solutions, impacting applications ranging from consumer audio to professional music production and assistive listening technologies. The field is also exploring unsupervised and semi-supervised learning methods to address data scarcity challenges in multi-channel audio enhancement.