Onset Detection

Onset detection, the identification of event beginnings in various signals, is a crucial task across diverse fields, aiming for accurate and efficient algorithms. Current research focuses on improving the robustness and speed of onset detection using deep learning models, particularly convolutional and recurrent neural networks, often incorporating multi-task learning and self-supervised pre-training to address data limitations. Applications range from medical diagnostics (e.g., epileptic seizure detection from video) to music information retrieval (e.g., automatic music transcription and query-by-humming systems), highlighting the broad impact of improved onset detection methods. Challenges remain in handling subtle or "soft" onsets, particularly in audio signals, necessitating further research into robust feature extraction and annotation techniques.