PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning to Align Polyphonic Music
Shai Shalev-Shwartz, Joseph Keshet and Yoram Singer
In: ISMIR 2004, October 10-14, 2004, Barcelona, Spain.


We describe an efficient learning algorithm for aligning a symbolic representation of a musical piece with its acoustic counterpart. Our method employs a supervised learning approach by using a training set of aligned symbolic and acoustic representations. The alignment function we devise is based on mapping the input acoustic-symbolic representation along with the target alignment into an abstract vector-space. Building on techniques used for learning support vector machines (SVM), our alignment function distills to a classifier in the abstract vector-space which separates correct alignments from incorrect ones. We describe a simple iterative algorithm for learning the alignment function and discuss its formal properties. We use our method for aligning MIDI and MP3 representations of polyphonic recordings of piano music. We also compare our discriminative approach to a generative method based on a generalization of hidden Markov models. In all of our experiments, the discriminative method outperforms the HMM-based method.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:422
Deposited By:Shai Shalev-Shwartz
Deposited On:21 December 2004