Audio Processing Task

Audio processing research focuses on developing efficient and robust methods for analyzing and manipulating audio signals, addressing tasks like classification, enhancement, and quality assessment. Current efforts involve integrating traditional signal processing techniques (e.g., Fourier and Wavelet transforms) with deep learning models, including latent diffusion models and audio-language models, to improve accuracy and reduce computational demands, particularly with limited data. This field is crucial for advancing applications such as voice assistants, audio editing software, and sound event detection systems, driving improvements in both the quality and efficiency of these technologies. The development of novel metrics, like those leveraging audio-language models for quality assessment, is also a key area of focus.