Drum sound detection in polyphonic music with hidden Markov models
EURASIP Journal on Audio, Speech, and Music Processing
Stochastic feature selection in support vector machine based instrument recognition
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Single-channel speech separation based on long-short frame associated harmonic model
Digital Signal Processing
Tutorial on multimedia music signal processing
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Score-informed audio decomposition and applications
Proceedings of the 21st ACM international conference on Multimedia
| Hi-index | 0.00 |
The purpose of this article is to present new advances in music transcription and source separation with a focus on drum signals. A complete drum transcription system is described, which combines information from the original music signal and a drum track enhanced version obtained by source separation. In addition to efficient fusion strategies to take into account these two complementary sources of information, the transcription system integrates a large set of features, optimally selected by feature selection. Concurrently, the problem of drum track extraction from polyphonic music is tackled both by proposing a novel approach based on harmonic/noise decomposition and time/frequency masking and by improving an existing Wiener filtering-based separation method. The separation and transcription techniques presented are thoroughly evaluated on a large public database of music signals. A transcription accuracy between 64.5% and 80.3% is obtained, depending on the drum instrument, for well-balanced mixes, and the efficiency of our drum separation algorithms is illustrated in a comprehensive benchmark.