SPARSE MUSIC DECOMPOSITION ONTO A MIDI DICTIONARY DRIVEN BY STATISTICAL MUSIC KNOWLEDGE

Boyang Gao 1 Emmanuel Dellandréa 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : The general goal of music signal decomposition is to represent the music structure into a note level to provide val- uable semantic features for further music analysis tasks. In this paper, we propose a new method to sparsely decompose the music signal onto a MIDI dictionary made of musical notes. Statistical music knowledge is further integrated into the whole sparse decomposition process. The proposed method is divided into a frame level sparse decomposition stage and a whole music level optimal note path searching. In the first stage note co-occurrence probabilities are embedded to generate a sparse multiple candidate graph while in the second stage note transition probabilities are incorporated into the optimal path searching. Experiments on real-world polyphonic music show that embedding music knowledge within the sparse decomposition achieves notable improvement in terms of note recognition precision and recall.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01339270
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Submitted on : Wednesday, June 29, 2016 - 3:51:06 PM
Last modification on : Thursday, November 21, 2019 - 2:12:29 AM

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  • HAL Id : hal-01339270, version 1

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Boyang Gao, Emmanuel Dellandréa, Liming Chen. SPARSE MUSIC DECOMPOSITION ONTO A MIDI DICTIONARY DRIVEN BY STATISTICAL MUSIC KNOWLEDGE. International Society for Music Information Retrieval Conference (ISMIR), Nov 2013, Curitiba, Brazil. pp.445-450. ⟨hal-01339270⟩

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