C4.5: programs for machine learning
C4.5: programs for machine learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Perception of Material from Contact Sounds
Presence: Teleoperators and Virtual Environments
MPEG-7 sound-recognition tools
IEEE Transactions on Circuits and Systems for Video Technology
Nearest-neighbor automatic sound annotation with a WordNet taxonomy
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Semiotics of Sounds Evoking Motions: Categorization and Acoustic Features
Computer Music Modeling and Retrieval. Sense of Sounds
What/when causal expectation modelling applied to audio signals
Connection Science - Music, Brain, Cognition
Analytical features: a knowledge-based approach to audio feature generation
EURASIP Journal on Audio, Speech, and Music Processing
Environmental sound recognition with time-frequency audio features
IEEE Transactions on Audio, Speech, and Language Processing
Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic score scene detection for baseball video
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
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We present a comparative evaluation of automatic classification of a sound database containing more than six hundred drum sounds (kick, snare, hihat, toms and cymbals). A preliminary set of fifty descriptors has been refined with the help of different techniques and some final reduced sets including around twenty features have been selected as the most relevant. We have then tested different classification techniques (instance-based, statistical-based, and tree-based) using ten-fold cross-validation. Three levels of taxonomic classification have been tested: membranes versus plates (super-category level), kick vs. snare vs. hihat vs. toms vs. cymbals (basic level), and some basic classes (kick and snare) plus some sub-classes -i.e. ride, crash, open-hihat, closed hihat, high-tom, medium-tom, low-tom- (sub-category level). Very high hit-rates have been achieved (99%, 97%, and 90% respectively) with several of the tested techniques.