Parallel distributed processing: explorations in the microstructure, vol. 2: psychological and biological models
Learning the Long-Term Structure of the Blues
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
Factor Oracle: A New Structure for Pattern Matching
SOFSEM '99 Proceedings of the 26th Conference on Current Trends in Theory and Practice of Informatics on Theory and Practice of Informatics
Using Factor Oracles for Machine Improvisation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Towards automatic transcription of expressive oral percussive performances
Proceedings of the 10th international conference on Intelligent user interfaces
Separate Neural Processing of Timbre Dimensions in Auditory Sensory Memory
Journal of Cognitive Neuroscience
Neural Computation
Sound onset detection by applying psychoacoustic knowledge
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
A supervised classification algorithm for note onset detection
EURASIP Journal on Applied Signal Processing
Unsupervised analysis and generation of audio percussion sequences
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
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A causal system to represent a stream of music into musical events, and to generate further expected events, is presented. Starting from an auditory front-end that extracts low-level (i.e. MFCC) and mid-level features such as onsets and beats, an unsupervised clustering process builds and maintains a set of symbols aimed at representing musical stream events using both timbre and time descriptions. The time events are represented using inter-onset intervals relative to the beats. These symbols are then processed by an expectation module using Predictive Partial Match, a multiscale technique based on N-grams. To characterise the ability of the system to generate an expectation that matches both ground truth and system transcription, we introduce several measures that take into account the uncertainty associated with the unsupervised encoding of the musical sequence. The system is evaluated using a subset of the ENST-drums database of annotated drum recordings. We compare three approaches to combine timing (when) and timbre (what) expectation. In our experiments, we show that the induced representation is useful for generating expectation patterns in a causal way.