Recurrent Neural Networks for Drum Transcription

Description

Music transcription is a core task in the field of music information retrieval. Transcribing the drum tracks of mu- sic pieces is a well-defined sub-task. The symbolic repre- sentation of a drum track contains much useful information about the piece, like meter, tempo, as well as various style and genre cues. This work introduces a novel approach for drum transcription using recurrent neural networks. We claim that recurrent neural networks can be trained to iden- tify the onsets of percussive instruments based on general properties of their sound. Different architectures of recur- rent neural networks are compared and evaluated using a well-known dataset. The outcomes are compared to results of a state-of-the-art approach on the same dataset. Further- more, the ability of the networks to generalize is demon- strated using a second, independent dataset. The exper- iments yield promising results: while F-measures higher than state-of-the-art results are achieved, the networks are capable of generalizing reasonably well.

Period	10 Aug 2016
Event title	17th International Society for Music Information Retrieval Conference (ISMIR)
Event type	Conference
Location	United StatesShow on map