Computers and musical style
A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites
WABI '01 Proceedings of the First International Workshop on Algorithms in Bioinformatics
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Looking for new, not known music only: music retrieval by melody style
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Computer Music Journal
Genre classification using chords and stochastic language models
Connection Science - Music, Brain, Cognition
Applying subgroup discovery for the analysis of string quartet movements
Proceedings of 3rd international workshop on Machine learning and music
Comparative pattern analysis of cretan folk songs
Proceedings of 3rd international workshop on Machine learning and music
| Hi-index | 0.00 |
This paper proposes a new view of pattern discovery in music: inductive querying a corpus for maximally general distinctive patterns. A pattern is distinctive if it is over-represented with respect to an anticorpus, and maximally general distinctive if no subsuming pattern is also distinctive. An algorithm for maximally general distinctive pattern discovery is presented and applied to folk song melodies from three geographic regions, and to chord sequences from three music genres. Distinctive patterns are applicable to a wide range of music analysis tasks where an anticorpus can be defined and contrasted with an analysis corpus.