Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Modern Information Retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Music structure based vector space retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A music search engine built upon audio-based and web-based similarity measures
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Using natural language input and audio analysis for a human-oriented MIR system
WEDELMUSIC'02 Proceedings of the Second international conference on Web delivering of music
"The way it Sounds": timbre models for analysis and retrieval of music signals
IEEE Transactions on Multimedia
Personalization in multimodal music retrieval
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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We extend an approach to search inside large-scale music collections by enabling the user to give feedback on the retrieved music pieces. In the original approach, a search engine that can be queried through free-form natural language text is automatically built upon audio-based and Web-based similarity measures. Features for music pieces in the collection are derived automatically by retrieving relevant Web pages via Google queries and using the contents of these pages to construct term vectors. The additional use of information about acoustic similarity allows for reduction of the dimensionality of the vector space and characterization of audio pieces with no associated Web information. With the incorporation of relevance feedback, the retrieval of pieces can be adapted according to the preferences of the user and thus compensate for inadequately represented initial queries. The approach is evaluated on a collection comprising about 12,000 pieces by using semantic tags provided by Audioscrobblerand a user study which also gives further insights into users search behaviors.