@inproceedings{eaef90d2034c4bf7a003eb7aa647e6d4,
title = "Bilevel sparse models for polyphonic music transcription",
abstract = "In this work, we propose a trainable sparse model for automatic polyphonic music transcription, which incorporates several successful approaches into a unified optimization framework. Our model combines unsupervised synthesis models similar to latent component analysis and nonnegative factorization with metric learning techniques that allow supervised discriminative learning. We develop efficient stochastic gradient training schemes allowing unsupervised, semi-, and fully supervised training of the model as well its adaptation to test data. We show efficient fixed complexity and latency approximation that can replace iterative minimization algorithms in time-critical applications. Experimental evaluation on synthetic and real data shows promising initial results.",
author = "Yakar, {Tal Ben} and Roee Litman and Pablo Sprechmann and Alex Bronstein and Guillermo Sapiro",
note = "Publisher Copyright: {\textcopyright} 2013 International Society for Music Information Retrieval.; 14th International Society for Music Information Retrieval Conference, ISMIR 2013 ; Conference date: 04-11-2013 Through 08-11-2013",
year = "2013",
language = "الإنجليزيّة",
series = "Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013",
publisher = "International Society for Music Information Retrieval",
pages = "65--70",
editor = "Britto, {Alceu de Souza} and Fabien Gouyon and Simon Dixon",
booktitle = "Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013",
}