An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Lightweight measures for timbral similarity of musical audio
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Incorporating machine-learning into music similarity estimation
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Aggregate features and ADABOOST for music classification
Machine Learning
A model-based approach to constructing music similarity functions
EURASIP Journal on Applied Signal Processing
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We address the problem of estimating automatically from audio signals the similarity between two pieces of music, a technology that has many applications in the online digital music industry. Conventional methods of audio music search use distance measures between features derived from the audio for this task. We describe three techniques that make use of music classifiers to derive representations of audio features that are based on culturally motivated information learned by the classifier. When these representations are used for similarity estimation, they produce very significant reductions in computational complexity over existing techniques (such as those based on the KL-Divergence), and also produce metric similarity spaces, which facilitate the use of technologies for the sub-linear scaling of search times. We have evaluated each system using both pseudo-objective techniques and human listeners, and we demonstrate that this efficiency gain is obtained while providing a comparable level of performance when compared with existing techniques.