Learning and clean-up in a large scale music database

Lars Kai Hansen; Tue Lehn-Schiøler; Kaare Brandt Petersen; Jeronimo Arenas-Garcia; Jan Larsen; Søren Holdt Jensen

Learning and clean-up in a large scale music database

Lars Kai Hansen
, Tue Lehn-Schiøler
, Kaare Brandt Petersen
, Jeronimo Arenas-Garcia
, Jan Larsen
, Søren Holdt Jensen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research

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Abstract

We have collected a database of musical features from radio broadcasts (N > 100.000). The database poses a number of hard modeling challenges including: Segmentation problems and missing metadata. We describe our efforts towards cleaning the database using signal processing and machine learning tools.

Original language	English
Title of host publication	EUSIPCO 2007
Publication date	2007
Publication status	Published - 2007
Event	EUSIPCO 2007 - Duration: 1 Jan 2007 → …

Conference

Conference	EUSIPCO 2007
Period	01/01/2007 → …

Keywords

Databases
Multimedia databases

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@inproceedings{30a2f7b1fb4846069394b676da37ab46,

title = "Learning and clean-up in a large scale music database",

abstract = "We have collected a database of musical features from radio broadcasts (N > 100.000). The database poses a number of hard modeling challenges including: Segmentation problems and missing metadata. We describe our efforts towards cleaning the database using signal processing and machine learning tools.",

keywords = "Databases, Multimedia databases",

author = "Hansen, \{Lars Kai\} and Tue Lehn-Schi{\o}ler and Petersen, \{Kaare Brandt\} and Jeronimo Arenas-Garcia and Jan Larsen and Jensen, \{S{\o}ren Holdt\}",

year = "2007",

language = "English",

booktitle = "EUSIPCO 2007",

note = "EUSIPCO 2007 ; Conference date: 01-01-2007",

}

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T1 - Learning and clean-up in a large scale music database

AU - Hansen, Lars Kai

AU - Lehn-Schiøler, Tue

AU - Petersen, Kaare Brandt

AU - Arenas-Garcia, Jeronimo

AU - Larsen, Jan

AU - Jensen, Søren Holdt

PY - 2007

Y1 - 2007

N2 - We have collected a database of musical features from radio broadcasts (N > 100.000). The database poses a number of hard modeling challenges including: Segmentation problems and missing metadata. We describe our efforts towards cleaning the database using signal processing and machine learning tools.

AB - We have collected a database of musical features from radio broadcasts (N > 100.000). The database poses a number of hard modeling challenges including: Segmentation problems and missing metadata. We describe our efforts towards cleaning the database using signal processing and machine learning tools.

KW - Databases

KW - Multimedia databases

M3 - Article in proceedings

BT - EUSIPCO 2007

T2 - EUSIPCO 2007

Y2 - 1 January 2007

ER -

Learning and clean-up in a large scale music database

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Keywords

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