Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
TIME '97 Proceedings of the 4th International Workshop on Temporal Representation and Reasoning (TIME '97)
Replacing suffix trees with enhanced suffix arrays
Journal of Discrete Algorithms - SPIRE 2002
A Normalized Levenshtein Distance Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to play like the great pianists
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Self-organizing multiple models for imitation: teaching a robot to dance the YMCA
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Melody Transcription From Music Audio: Approaches and Evaluation
IEEE Transactions on Audio, Speech, and Language Processing
A Groovy Virtual Drumming Agent
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
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The drum machine has been an important tool in music production for decades. However, its flawless way of playing drum patterns is often perceived as mechanical and rigid, far from the groove provided by a human drummer. This paper presents research towards enhancing the drum machine with learning capabilities. The drum machine learns user-specific variations (i.e. the groove) from human drummers, and stores the groove as attractors in Echo State Networks (ESNs). The ESNs are purely generative (i.e. not driven by an input signal) and the output is used by the drum machine to imitate the playing style of human drummers, making it a cost-effective way of achieving life-like drums.