The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Combination of audio and lyrics features for genre classification in digital audio collections
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Map-based music interfaces for mobile devices
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Towards an automatically generated music information system via web content mining
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Artist filtering for non-western music classification
Proceedings of the 6th Audio Mostly Conference: A Conference on Interaction with Sound
Music genre classification using LBP textural features
Signal Processing
Context-Aware features for singing voice detection in polyphonic music
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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With increasing amounts of music being available in digital form, research in music information retrieval has turned into a dominant field to support organization of and easy access to large collections of music. Yet, most research is focussed traditionally on Western music, mostly in the form of mastered studio recordings. This leaves the question whether current music information retrieval approaches can also be applied to collections of non-Western and in particular ethnic music with completely different characteristics and requirements. In this work we analyze the performance of a range of automatic audio description algorithms on three music databases with distinct characteristics, specifically a Western music collection used previously in research benchmarks, a collection of Latin American music with roots in Latin American culture, but following Western tonality principles, as well as a collection of field recordings of ethnic African music. The study quantitatively shows the advantages and shortcomings of different feature representations extracted from music on the basis of classification tasks, and presents an approach to visualize, access and interact with ethnic music collections in a structured way.