A model for reasoning about persistence and causation
Computational Intelligence
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
An overview of audio information retrieval
Multimedia Systems - Special issue on audio and multimedia
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Looking for new, not known music only: music retrieval by melody style
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Drum loops retrieval from spoken queries
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Effective retrieval of polyphonic audio with polyphonic symbolic queries
Proceedings of the international workshop on Workshop on multimedia information retrieval
Tuning the Feature Space for Content-Based Music Retrieval
Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
Adaptive content-based music retrieval system
Multimedia Tools and Applications
Segmentation, indexing, and retrieval for environmental and natural sounds
IEEE Transactions on Audio, Speech, and Language Processing
On musical performances identification, entropy and string matching
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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Query by melody is the problem of retrieving musical performances from melodies. Retrieval of real performances is complicated due to the large number of variations in performing a melody and the presence of colored accompaniment noise. We describe a simple yet effective probabilistic model for this task. We describe a generative model that is rich enough to capture the spectral and temporal variations of musical performances and allows for tractable melody retrieval. While most of previous studies on music retrieval from melodies were performed with either symbolic (e.g. MIDI) data or with monophonic (single instrument) performances, we performed experiments in retrieving live and studio recordings of operas that contain a leading vocalist and rich instrumental accompaniment. Our results show that the probabilistic approach we propose is effective and can be scaled to massive datasets.