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Communications of the ACM
Machine rhythm: computer emulation of human rhythm perception
Machine rhythm: computer emulation of human rhythm perception
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Towards the digital music library: tune retrieval from acoustic input
Proceedings of the first ACM international conference on Digital libraries
Computer-based recognition of musical phrases using the rough-set approach
Information Sciences: an International Journal - From rough sets to soft computing
Multilingual keyword extraction for term suggestion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Content-based retrieval for music collections
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Problems of music information retrieval in the real world
Information Processing and Management: an International Journal
Data Mining: Machine Learning, Statistics, and Databases
SSDBM '96 Proceedings of the Eighth International Conference on Scientific and Statistical Database Management
Rhythm Quantization for Transcription
Computer Music Journal
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
Searching for Metric Structure of Musical Files
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Automatic Rhythm Retrieval from Musical Files
Transactions on Rough Sets IX
The domain of acoustics seen from the rough sets perspective
Transactions on rough sets VI
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This article describes experimental work carried out in attempt to improve the effectiveness of musical rhythm retrieval systems. The authors define basic notions in the area of hierarchical rhythm retrieval and describe a procedure for inducing rhythmic hypotheses in a given melody. Utilizing an approach commonly used in the data mining domain, an association rule model has been applied to estimate the rhythmic salience of sounds based on the physical attributes of duration, frequency and amplitude. On the basis of the knowledge obtained by the machine learning system, the authors propose five functions to rank sounds according to their tendency to be located in accented positions in a melody. Adapted precision and recall measures were used to validate the proposed functions and conduct experimental verification. Conclusions derived from the results of the experiments have also been presented.