Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Fundamentals of speech recognition
Fundamentals of speech recognition
Nonlinear time series analysis
Nonlinear time series analysis
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Classification of general audio data for content-based retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Content-based organization and visualization of music archives
Proceedings of the tenth ACM international conference on Multimedia
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Survey of Text Mining
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
Anchor space for classification and similarity measurement of music
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Modeling timbre distance with temporal statistics from polyphonic music
IEEE Transactions on Audio, Speech, and Language Processing
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
Searching musical audio datasets by a batch of multi-variant tracks
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Analytical features: a knowledge-based approach to audio feature generation
EURASIP Journal on Audio, Speech, and Music Processing
Local summarization and multi-level LSH for retrieving multi-variant audio tracks
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Classification of household devices by electricity usage profiles
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
A shapelet transform for time series classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Alternative quality measures for time series shapelets
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Classification accuracy is not enough
Journal of Intelligent Information Systems
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Data mining in large collections of polyphonic music has recently received increasing interest by companies along with the advent of commercial online distribution of music. Important applications include the categorization of songs into genres and the recommendation of songs according to musical similarity and the customer's musical preferences. Modeling genre or timbre of polyphonic music is at the core of these tasks and has been recognized as a difficult problem. Many audio features have been proposed, but they do not provide easily understandable descriptions of music. They do not explain why a genre was chosen or in which way one song is similar to another. We present an approach that combines large scale feature generation with meta learning techniques to obtain meaningful features for musical similarity. We perform exhaustive feature generation based on temporal statistics and train regression models to summarize a subset of these features into a single descriptor of a particular notion of music. Using several such models we produce a concise semantic description of each song. Genre classification models based on these semantic features are shown to be better understandable and almost as accurate as traditional methods.