Spectro Temporal

Spectro-temporal analysis focuses on extracting meaningful information from audio signals by considering both the frequency content (spectrum) and its evolution over time. Current research emphasizes the development of deep learning models, such as convolutional neural networks and recurrent neural networks, often incorporating multi-resolution and multi-path architectures to effectively capture complex spectro-temporal patterns. These techniques are applied across diverse applications, including speech recognition (especially for disordered or elderly speech), sound event classification, and music information retrieval, improving accuracy and enabling new forms of audio analysis and understanding. The resulting advancements have significant implications for various fields, from assistive technologies to mental health diagnostics.