Beat Tracking

Beat tracking, the automated identification of rhythmic pulses in audio signals, aims to accurately locate beats and downbeats in music and other time-series data like electrocardiograms (ECGs). Current research emphasizes the development of robust algorithms, often employing deep learning architectures such as Transformers and Convolutional Neural Networks, to handle diverse musical styles and noisy data, including the integration of beat information into music generation and dance synthesis. These advancements have significant implications for music information retrieval, medical signal processing (e.g., improved ECG analysis and heart rate monitoring), and creative applications like music generation and dance choreography.