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Large-Scale Pattern Discovery in Music.

Bertin-Mahieux, Thierry.

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Large-Scale Pattern Discovery in Music.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Large-Scale Pattern Discovery in Music./
作者:	Bertin-Mahieux, Thierry.
面頁冊數:	126 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.
Contained By:	Dissertation Abstracts International74-06B(E).
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3552555
ISBN:	9781267910837

Large-Scale Pattern Discovery in Music.
Bertin-Mahieux, Thierry.

Large-Scale Pattern Discovery in Music. - 126 p.

Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2013.

This work focuses on extracting patterns in musical data from very large collections. The problem is split in two parts. First, we build such a large collection, the Million Song Dataset, to provide researchers access to commercial-size datasets. Second, we use this collection to study cover song recognition which involves finding harmonic patterns from audio features.

ISBN: 9781267910837Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Large-Scale Pattern Discovery in Music.
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500 $a Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.
500 $a Adviser: Daniel P. W. Ellis.
502 $a Thesis (Ph.D.)--Columbia University, 2013.
520 $a This work focuses on extracting patterns in musical data from very large collections. The problem is split in two parts. First, we build such a large collection, the Million Song Dataset, to provide researchers access to commercial-size datasets. Second, we use this collection to study cover song recognition which involves finding harmonic patterns from audio features.
520 $a Regarding the Million Song Dataset, we detail how we built the original collection from an online API, and how we encouraged other organizations to participate in the project. The result is the largest research dataset with heterogeneous sources of data available to music technology researchers. We demonstrate some of its potential and discuss the impact it already has on the field.
520 $a On cover song recognition, we must revisit the existing literature since there are no publicly available results on a dataset of more than a few thousand entries. We present two solutions to tackle the problem, one using a hashing method, and one using a higher-level feature computed from the chromagram (dubbed the 2DFTM). We further investigate the 2DFTM since it has potential to be a relevant representation for any task involving audio harmonic content.
520 $a Finally, we discuss the future of the dataset and the hope of seeing more work making use of the different sources of data that are linked in the Million Song Dataset. Regarding cover songs, we explain how this might be a first step towards defining a harmonic manifold of music, a space where harmonic similarities between songs would be more apparent.
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