Sound Event

Sound event detection (SED) focuses on identifying the types and temporal locations of sounds within audio recordings, a crucial task with applications in various fields. Current research emphasizes improving SED robustness to overlapping sounds and noisy environments, often employing deep learning models like convolutional recurrent neural networks (CRNNs) and transformers, along with techniques such as audio source separation and multi-modal data fusion (audio-visual). These advancements are driving progress in areas like smart home monitoring, environmental monitoring, and assistive technologies for older adults, highlighting the growing importance of accurate and efficient sound event analysis.