Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 1998 conference on Advances in neural information processing systems II
HMM-based musical query retrieval
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
ICML '06 Proceedings of the 23rd international conference on Machine learning
Music retrieval: a tutorial and review
Foundations and Trends in Information Retrieval
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards musical query-by-semantic-description using the CAL500 data set
SIGIR '07 Proceedings of the 30th 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
Introduction to Information Retrieval
Introduction to Information Retrieval
Combining audio content and social context for semantic music discovery
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Exploring automatic music annotation with "acoustically-objective" tags
Proceedings of the international conference on Multimedia information retrieval
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Analysis of Minimum Distances in High-Dimensional Musical Spaces
IEEE Transactions on Audio, Speech, and Language Processing
Semantic Annotation and Retrieval of Music and Sound Effects
IEEE Transactions on Audio, Speech, and Language Processing
Quantitative Analysis of a Common Audio Similarity Measure
IEEE Transactions on Audio, Speech, and Language Processing
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery
IEEE Transactions on Multimedia
Time Series Models for Semantic Music Annotation
IEEE Transactions on Audio, Speech, and Language Processing
Modeling concept dynamics for large scale music search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Folksonomy link prediction based on a tripartite graph for tag recommendation
Journal of Intelligent Information Systems
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The rise of the Internet has led the music industry to a transition from physical media to online products and services. As a consequence, current online music collections store millions of songs and are constantly being enriched with new content. This has created a need for music technologies that allow users to interact with these extensive collections efficiently and effectively. Music search and discovery may be carried out using tags, matching user interests and exploiting content-based acoustic similarity. One major issue in music information retrieval is how to combine such noisy and heterogeneous information sources in order to improve retrieval effectiveness. With this aim in mind, the article explores a novel music retrieval framework based on combining tags and acoustic similarity through a probabilistic graph-based representation of a collection of songs. The retrieval function highlights the path across the graph that most likely observes a user query and is used to improve state-of-the-art music search and discovery engines by delivering more relevant ranking lists. Indeed, by means of an empirical evaluation, we show how the proposed approach leads to better performances than retrieval strategies which rank songs according to individual information sources alone or which use a combination of them.