In this paper we explore the relationship between the temporal and rhythmic structure of musical audio signals. Using automatically extracted rhythmic structure we present a rhythmically-aware method to combine note onset detection techniques. Our method uses topdown knowledge of repetitions of musical events to improve detection performance by modelling the temporal distribution of onset locations. Results on a publicly available database demonstrate that using musical knowledge in this way can lead to significant improvements by reducing the number of missed and spurious detections.
In previous work REF , we proposed a rhythmically aware onset detection algorithm.
Note onset detection using rhythmic structure
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
Computer Science