Audio Quality Assessment

Audio quality assessment aims to objectively measure how well an audio signal sounds, typically by predicting human perception of quality without the need for extensive listening tests. Current research focuses on developing robust reference-free and no-reference metrics, employing deep learning architectures like bidirectional LSTMs and generative models, often leveraging pre-trained audio-language models or self-supervised learning techniques to capture perceptual features. These advancements are crucial for improving various audio processing tasks, such as speech enhancement, hearing aid technology, and generative audio modeling, by providing efficient and accurate quality evaluation tools.