Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
MP3: The Definitive Guide
Self-Organizing Maps
Name that tune: a pilot study in finding a melody from a sung query
Journal of the American Society for Information Science and Technology
A statistical approach to retrieval under user-dependent uncertainty in query-by-humming systems
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
An architecture for effective music information retrieval
Journal of the American Society for Information Science and Technology - Music information retrieval
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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A plug-in for the Machine Learning Environment Yale has been developed that automatically structures digital music corpora into similarity clusters using a SOM on the basis of features that are extracted from files in a test corpus. Perceptionally similar music files are represented in the same cluster. A human user was asked to rate music files according to their subjective similarity. Compared to the user's judgment, the system had a mean accuracy of 65.7%. The accuracy of the framework increases with the size of the music corpus to a maximum of 75%. The study at hand shows that it is possible to categorize music files into similarity clusters by taking solely mathematical features into account that have been extracted from the files themselves. This allows for a variety of different applications like lowering the search space in manual music comparison, or content-based music recommendation.