An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
On the Estimation of 'Small' Probabilities by Leaving-One-Out
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parsing with Probabilistic Strictly Locally Testable Tree Languages
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree Representation in Combined Polyphonic Music Comparison
Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music
Journal of Computer and System Sciences
Smoothing and compression with stochastic k-testable tree languages
Pattern Recognition
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.