Learning to align polyphonic music

Shai Shalev-Shwartz, Joseph Keshet, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 acousticsymbolic 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 vectorspace 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.
Original languageEnglish
Title of host publicationISMIR 2004
Number of pages6
StatePublished - 2004
Event5th International Conference on Music Information Retrieval, ISMIR 2004 - Barcelona, Spain
Duration: 10 Oct 200414 Oct 2004
Conference number: 5


Conference5th International Conference on Music Information Retrieval, ISMIR 2004
Abbreviated titleISMIR 2004


Dive into the research topics of 'Learning to align polyphonic music'. Together they form a unique fingerprint.

Cite this