Automatic Extraction of Drum Tracks from Polyphonic Music Signals
CW '02 Proceedings of the First International Symposium on Cyber Worlds (CW'02)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Computer Music Journal
B-Keeper: a beat-tracker for live performance
NIME '07 Proceedings of the 7th international conference on New interfaces for musical expression
A hand clap interface for sonic interaction with the computer
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Bayesian inference for nonnegative matrix factorisation models
Computational Intelligence and Neuroscience
Self-produced sound: tightly binding haptics and audio
HAID'07 Proceedings of the 2nd international conference on Haptic and audio interaction design
Drum sound detection in polyphonic music with hidden Markov models
EURASIP Journal on Audio, Speech, and Music Processing
Synthesis of Hand Clapping Sounds
IEEE Transactions on Audio, Speech, and Language Processing
Rhythmic walking interactions with auditory feedback: an exploratory study
Proceedings of the 7th Audio Mostly Conference: A Conference on Interaction with Sound
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Interactive musical systems require real-time, low-latency, accurate, and reliable event detection and classification algorithms. In this paper, we introduce a model-based algorithm for detection of percussive events and test the algorithm on the detection and classification of different percussive sounds. We focus on tuning the algorithm for a good compromise between temporal precision, classification accuracy and low latency. The model is trained offline on different percussive sounds using the expectation maximization approach for learning spectral templates for each sound and is able to run online to detect and classify sounds from audio stream input by a Hidden Markov Model. Our results indicate that the approach is promising and applicable in design and development of interactive musical systems.