An expert system for harmonizing analysis of tonal music
Understanding music with AI
Towards the digital music library: tune retrieval from acoustic input
Proceedings of the first ACM international conference on Digital libraries
Music retrieval as text retrieval (poster abstract): simple yet effective
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Towards a digital library of popular music
Proceedings of the fourth ACM conference on Digital libraries
A Study of Musical Features for Melody Databases
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
Music Databases: Indexing Techniques and Implementation
IW-MMDBMS '96 Proceedings of the 1996 International Workshop on Multi-Media Database Management Systems (IW-MMDBMS '96)
Pattern Recognition Letters
Melody Track Selection Using Discriminative Language Model
IEICE - Transactions on Information and Systems
A kind of index for content-based music information retrieval and theme mining
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
A novel melody line identification algorithm for polyphonic MIDI music
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
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One major approach to music retrieval is to model music as a sequence of features, after which traditional information retrieval techniques are applied on the sequence. Because of the temporal nature of music and the inexactness of user queries, most effort on music retrieval systems focus on issues such as indexing and approximation match. In contrast, the processing of music before feature extraction, such as the identification of melody track, were often considered easy or done. This may be the case in a controlled environment, such as one for musicology research, where human beings carefully analyze the pieces before being submitted to the database. However, in an environment where large volumes of music are obtained from the Web, manual music analysis is impractical. Since many well-known musical features often pertain to the melody of musical pieces, and users often remember the melody of a song, algorithms that select the melody tracks of a piece are important for Web-based content-based retrieval systems. In this paper, we describe a number of algorithms for automatic melody track selection in a music retrieval context. We will also study the performance of the algorithms by comparing their answers to those judged by human beings.