Onset detection in pitched non-percussive music using warping-compensated correlation

Automatically extracting temporal information from musical recordings is inarguably one of the most critical subtasks of many music information retrieval systems. In this paper we present a system for automatic note onset detection in pitched non-percussive (PNP) musical sounds, which is the most challenging audio signal group for this task. We propose a new approach based on stable pitch cues and signal energy. A computationally inexpensive method for feature extraction, which efficiently suppresses vibrato, is combined with information derived from the signal energy in the feature space. Onsets are localized by a median filter based peak picking method. The proposed method is tested against a database of annotated violin recordings, covering a wide range of tempo and playing styles like vibrato and staccato. Our system outperforms prior state of the art systems with results for true positives of 91.2% and false positives of 9.2%.