Academia Sinica
type: Organization
member: Yi-Hsuan Yang

Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Spain
type: Organization
member: Joan Serrà

Artificial Intelligence Research Institute (IIIA-CSIC)
type: Organization
member: Joan Serrà

Austrian Research Institute for Artificial Intelligence
type: Organization
member: Arthur Flexer

BBC
type: Organization
member: Ian Knopke
name: British Broadcasting Corporation

Centre de Recerca Matemàtica
type: Organization
member: Álvaro Corral

Centre for Digital Music, Queen Mary University of London
type: Organization
member: Mathieu Barthet

City University London
type: Organization
member: Daniel Wolff
member: Tillman Weyde

Columbia University
type: Organization
member: Daniel P.W. Ellis
member: Thierry Bertin-Mahieux

CWI
type: Organization
member: Arjen De Vries

Department of Music, University of Aberdeen
type: Organization

DFKI & Technical University of Kaiserslautern
type: Organization
member: Stephan Baumann

Faculty of Science, University of Porto
type: Organization
member: Alípio Mário Jorge
member: João Vinagre
member: José Paulo Leal
member: Luís Lemos

Goldsmiths College, University of London
type: Organization
member: Geraint Wiggins

Gracenote
type: Organization
member: Ching-Wei Chen
member: Oscar Celma

INESC Porto
type: Organization
member: Fabien Gouyon

INESC TEC
type: Organization
member: Fabien Gouyon
member: Marcos Aurélio Domingues

Johannes Kepler University Linz
type: Organization
member: Dominik Schnitzer

Johannes Kepler University
type: Organization
member: David Hauger
member: Markus Schedl
member: Peter Knees

McGill University
type: Organization
label: McGill university
member: Andrew Hankinson
member: Gabriel Vigliensoni
member: Ichiro Fujinaga
member: John Ashley Burgoyne

Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
type: Organization
member: Emilia Gomez
member: Justin Salamon

Music Technology Group / Universitat Pompeu Fabra
type: Organization
label: Music Technology Group, Universitat Pompeu Fabra
member: Dmitry Bogdanov
member: Emilia Gomez
member: Mohamed Sordo

National Institute of Advanced Industrial Science and Technology (AIST)
type: Organization
member: Masataka Goto

National Network New Media Engineering Research Center，Institute of Acoustics, Chinese Academy of Sciences
type: Organization
member: Jun Wang

Otto-von-Guericke-University Magdeburg
type: Organization
member: Sebastian Stober

Queen Mary University of London
type: Organization
member: Robert Macrae

Tel-Aviv University
type: Organization
member: Noam Koenigstein

The Echo Nest
type: Organization
member: Paul Lamere

UCSD
type: Organization
member: Brian McFee
member: Gert Lanckriet

UIUC
type: Organization
member: Xiao Hu

Universitat Pompeu Fabra
type: Organization
member: Dmitry Bogdanov
member: Martin Haro
member: Mohamed Sordo
member: Perfecto Herrera

University Carlos III of Madrid
type: Organization
member: Julián Urbano

University of California, San Diego
type: Organization
member: Brian McFee

University of Miami
type: Organization
member: Mitsunori Ogihara

(unknown)
type: Organization
member: Ben Fields
member: Matthias Mauch
member: Mehdi Elahi
member: Steve Tjoa

Yahoo! Research
type: Organization
member: Malcolm Slaney

Alípio Mário Jorge
affiliation: Faculty of Science, University of Porto
type: Person
based near: Portugal
made: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail

Álvaro Corral
affiliation: Centre de Recerca Matemàtica
type: Person
based near: Spain
made: Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals

Andrew Hankinson
affiliation: McGill University
type: Person
based near: Canada
made: Creating Large-Scale Searchable Digital Collections from Printed Music Materials

Arjen De Vries
holds role: Programme committee member
affiliation: CWI
type: Person
based near: Netherlands

Arthur Flexer
holds role: Programme committee member
affiliation: Austrian Research Institute for Artificial Intelligence
type: Person
based near: Austria

Ben Fields
holds role: Programme committee member
affiliation: (unknown)
type: Person
based near: resource

Brian McFee
holds role: Programme committee member
affiliation: UCSD
affiliation: University of California, San Diego
type: Person
label: Brian Mcfee
based near: United States
made: The Million Song Dataset Challenge

Ching-Wei Chen
holds role: Programme committee member
affiliation: Gracenote
type: Person
based near: United States

Daniel P.W. Ellis
affiliation: Columbia University
type: Person
based near: United States
made: The Million Song Dataset Challenge

Daniel Wolff
affiliation: City University London
type: Person
based near: United Kingdom
made: Adapting Similarity on the MagnaTagATune Dataset: Effects of Model and Feature Choices

David Hauger
affiliation: Johannes Kepler University
type: Person
based near: Austria
made: Mining Microblogs to Infer Music Artist Similarity and Cultural Listening Patterns

Dmitry Bogdanov
holds role: Programme committee member
affiliation: Music Technology Group / Universitat Pompeu Fabra
affiliation: Universitat Pompeu Fabra
type: Person
based near: Spain
made: Music Retagging Using Label Propagation and Robust Principal Component Analysis

Dominik Schnitzer
holds role: Programme committee member
affiliation: Johannes Kepler University Linz
type: Person
based near: Austria

Emilia Gomez
holds role: Programme committee member
affiliation: Music Technology Group / Universitat Pompeu Fabra
affiliation: Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
type: Person
label: Emilia Gómez
based near: Spain
made: Melody, Bassline and Harmony Representations for Music Version Identification

Fabien Gouyon
holds role: Programme committee member
affiliation: INESC Porto
affiliation: INESC TEC
type: Person
based near: Portugal
made: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail

Gabriel Vigliensoni
affiliation: McGill University
type: Person
based near: Canada
made: Creating Large-Scale Searchable Digital Collections from Printed Music Materials

Geraint Wiggins
holds role: Programme committee member
affiliation: Goldsmiths College, University of London
type: Person
based near: United Kingdom

Gert Lanckriet
affiliation: UCSD
type: Person
based near: United States
made: The Million Song Dataset Challenge

Ian Knopke
holds role: Programme committee member
affiliation: BBC
type: Person
based near: United Kingdom

Ichiro Fujinaga
affiliation: McGill University
type: Person
based near: Canada
made: Creating Large-Scale Searchable Digital Collections from Printed Music Materials

Joan Serrà
affiliation: Artificial Intelligence Research Institute (IIIA-CSIC)
affiliation: Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Spain
type: Person
based near: Spain
made: Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals
made: Melody, Bassline and Harmony Representations for Music Version Identification

João Vinagre
affiliation: Faculty of Science, University of Porto
type: Person
based near: Portugal
made: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail

John Ashley Burgoyne
affiliation: McGill University
type: Person
based near: Canada
made: Creating Large-Scale Searchable Digital Collections from Printed Music Materials

José Paulo Leal
affiliation: Faculty of Science, University of Porto
type: Person
based near: Portugal
made: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail

Julián Urbano
holds role: Programme committee member
affiliation: University Carlos III of Madrid
type: Person
based near: Spain
made: Towards Minimal Test Collections for Evaluation of Audio Music Similarity and Retrieval

Jun Wang
holds role: Programme committee member
affiliation: National Network New Media Engineering Research Center，Institute of Acoustics, Chinese Academy of Sciences
type: Person
based near: China

Justin Salamon
affiliation: Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
type: Person
based near: Spain
made: Melody, Bassline and Harmony Representations for Music Version Identification

Luís Lemos
affiliation: Faculty of Science, University of Porto
type: Person
based near: Portugal
made: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail

Malcolm Slaney
holds role: Programme committee member
affiliation: Yahoo! Research
type: Person
based near: United States

Marcos Aurélio Domingues
affiliation: INESC TEC
type: Person
based near: Portugal
made: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail

Markus Schedl
holds role: Programme chair
affiliation: Johannes Kepler University
type: Person
based near: Austria
made: Towards Minimal Test Collections for Evaluation of Audio Music Similarity and Retrieval
made: Mining Microblogs to Infer Music Artist Similarity and Cultural Listening Patterns

Martin Haro
affiliation: Universitat Pompeu Fabra
type: Person
based near: Spain
made: Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals

Masataka Goto
holds role: Programme committee member
affiliation: National Institute of Advanced Industrial Science and Technology (AIST)
type: Person
based near: Japan

Mathieu Barthet
holds role: Programme committee member
affiliation: Centre for Digital Music, Queen Mary University of London
type: Person
based near: United Kingdom

Matthias Mauch
holds role: Programme committee member
affiliation: (unknown)
type: Person
based near: resource

Mehdi Elahi
holds role: Programme committee member
affiliation: (unknown)
type: Person
based near: resource

Mitsunori Ogihara
holds role: Programme committee member
affiliation: University of Miami
type: Person
based near: United States

Mohamed Sordo
holds role: Programme committee member
affiliation: Music Technology Group / Universitat Pompeu Fabra
affiliation: Universitat Pompeu Fabra
type: Person
based near: Spain
made: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail
made: Music Retagging Using Label Propagation and Robust Principal Component Analysis

Noam Koenigstein
holds role: Programme committee member
affiliation: Tel-Aviv University
type: Person
based near: Israel

Oscar Celma
holds role: Programme chair
affiliation: Gracenote
type: Person
based near: Spain

Paul Lamere
holds role: Programme committee member
affiliation: The Echo Nest
type: Person
based near: United States

Perfecto Herrera
affiliation: Universitat Pompeu Fabra
type: Person
based near: Spain
made: Music Retagging Using Label Propagation and Robust Principal Component Analysis
made: Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals

Peter Knees
holds role: Programme chair
affiliation: Johannes Kepler University
type: Person
based near: Austria

Robert Macrae
holds role: Programme committee member
affiliation: Queen Mary University of London
type: Person
based near: United Kingdom

Sebastian Stober
holds role: Programme committee member
affiliation: Otto-von-Guericke-University Magdeburg
type: Person
based near: Germany

Stephan Baumann
holds role: Programme committee member
affiliation: DFKI & Technical University of Kaiserslautern
type: Person
based near: Germany

Steve Tjoa
holds role: Programme committee member
affiliation: (unknown)
type: Person
based near: resource

Thierry Bertin-Mahieux
affiliation: Columbia University
type: Person
based near: United States
made: The Million Song Dataset Challenge

Tillman Weyde
affiliation: City University London
type: Person
based near: United Kingdom
made: Adapting Similarity on the MagnaTagATune Dataset: Effects of Model and Feature Choices

Xiao Hu
holds role: Programme committee member
affiliation: UIUC
type: Person
based near: United States

Yi-Hsuan Yang
affiliation: Academia Sinica
type: Person
based near: Taiwan
made: Music Retagging Using Label Propagation and Robust Principal Component Analysis

The Million Song Dataset Challenge
is part of: proceedings
creator: Brian McFee
creator: Daniel P.W. Ellis
creator: Gert Lanckriet
creator: Thierry Bertin-Mahieux
subject: evaluation
subject: large-scale
subject: music recommendation
author list: authorlist
abstract: Music is not a patio chair or a cutlery set, and it should not be recommended the same way. Music is a highly complex signal that is part of our cultural framework and has a prominent place in our everyday life. Yet dealing with the millions of songs we have access to and finding the ones that will enliven our day is essential. What could once be solved by advice from one’s best friends has evolved into a large-scale internet-era problem, and the music information retrieval community needs to step up to the challenge. We introduce the Million Song Dataset Challenge to improve the state-of-the-art and unify the community behind a common evaluation. The goal is to predict the songs a user will listen to, given the user’s listening history and full information (meta-data, content analysis) of all songs. We explain the need for such a contest, the data we use, our goals and design choices, and present baseline experimental results using simple, off-the-shelf recommendation algorithms.
year: 2012
type: InProceedings
maker: Brian McFee
maker: Daniel P.W. Ellis
maker: Gert Lanckriet
maker: Thierry Bertin-Mahieux

Towards Minimal Test Collections for Evaluation of Audio Music Similarity and Retrieval
is part of: proceedings
creator: Julián Urbano
creator: Markus Schedl
subject: evaluation
subject: music information retrieval
subject: relevance judgments
subject: test collections
author list: authorlist
abstract: Reliable evaluation of Information Retrieval systems requires large amounts of relevance judgments. Making these annotations is quite complex and tedious for many Music Information Retrieval tasks, so performing such evaluations requires too much effort. A low-cost alternative is the application of Minimal Test Collection algorithms, which offer quite reliable results while significantly reducing the annotation effort. The idea is to incrementally select what documents to judge so that we can compute estimates of the effectiveness differences between systems with some degree of confidence. In this paper we show a first approach towards its application to the evaluation of the Audio Music Similarity and Retrieval task. An analysis with the MIREX 2009 and 2011 data shows that the judging effort can be reduced to about 30-40% to obtain results with 95% confidence.
year: 2012
type: InProceedings
maker: Julián Urbano
maker: Markus Schedl

Creating Large-Scale Searchable Digital Collections from Printed Music Materials
is part of: proceedings
creator: Andrew Hankinson
creator: Gabriel Vigliensoni
creator: Ichiro Fujinaga
creator: John Ashley Burgoyne
subject: digital collections
subject: music notation
subject: optical music recognition
subject: search
subject: web applications
author list: authorlist
abstract: In this paper we present our work towards developing a large-scale web application for digitizing, recognizing (via optical music recognition), correcting, displaying, and searching printed music texts. We present the results of a recently completed prototype implementation of our workflow process, from document capture to presentation on the web. We discuss a number of lessons learned from this prototype. Finally, we present some open-source Web 2.0 tools developed to provide essential infrastructure components for making searchable printed music collections available online. Our hope is that these experiences and tools will help in creating next-generation globally accessible digital music libraries.
year: 2012
type: InProceedings
maker: Andrew Hankinson
maker: Gabriel Vigliensoni
maker: Ichiro Fujinaga
maker: John Ashley Burgoyne

Mining Microblogs to Infer Music Artist Similarity and Cultural Listening Patterns
is part of: proceedings
creator: David Hauger
creator: Markus Schedl
subject: evaluation
subject: music information retrieval
subject: similarity measurement
subject: social media mining
author list: authorlist
abstract: This paper aims at leveraging microblogs to address two challenges in music information retrieval (MIR), similarity estimation between music artists and inferring typical listening patterns at different granularity levels (city, country, global). From two collections of several million microblogs, which we gathered over ten months, music-related information is extracted and statistically analyzed. We propose and evaluate four co-occurrence-based methods to compute artist similarity scores. Moreover, we derive and analyze culture-specific music listening patterns to investigate the diversity of listening behavior around the world.
year: 2012
type: InProceedings
maker: David Hauger
maker: Markus Schedl

Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail
is part of: proceedings
creator: Alípio Mário Jorge
creator: Fabien Gouyon
creator: João Vinagre
creator: José Paulo Leal
creator: Luís Lemos
creator: Marcos Aurélio Domingues
creator: Mohamed Sordo
subject: Audio features
subject: Hybrid recommender system
subject: Music recommendation
subject: Tags
subject: Usage data
author list: authorlist
abstract: In this paper we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real time on a commercial music web site, specialised in content from the very long tail of music content. We compare it against two stand-alone recommenders, the first system based on usage and the second one based on content data. The results show that the proposed hybrid recommender shows advantages with respect to usage- and content-based systems, namely, higher user absolute acceptance rate, higher user activity rate and higher user loyalty.
year: 2012
type: InProceedings
maker: Alípio Mário Jorge
maker: Fabien Gouyon
maker: João Vinagre
maker: José Paulo Leal
maker: Luís Lemos
maker: Marcos Aurélio Domingues
maker: Mohamed Sordo

Music Retagging Using Label Propagation and Robust Principal Component Analysis
is part of: proceedings
creator: Dmitry Bogdanov
creator: Mohamed Sordo
creator: Perfecto Herrera
creator: Yi-Hsuan Yang
subject: music auto-tagging
subject: music retagging
subject: social tagging
subject: tag-based music retrieval
author list: authorlist
abstract: The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them to construct machine learning models. Nevertheless, such tags are usually noisy and sparse. In this paper, we present a preliminary study that aims at refining (retagging) social tags by exploiting the content similarity between tracks and the semantic redundancy of the track-tag matrix. The evaluated algorithms include a graph-based label propagation method that is often used in semi-supervised learning and a robust principal component analysis (PCA) algorithm that has led to state-of-the-art result in matrix completion. The result indicates that robust PCA with content similarity constraint is particularly effective; it improves the robustness of tagging against three types of synthetic errors and boosts the recall rate of music auto-tagging by 7% in a real-world setting.
year: 2012
type: InProceedings
maker: Dmitry Bogdanov
maker: Mohamed Sordo
maker: Perfecto Herrera
maker: Yi-Hsuan Yang

Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals
is part of: proceedings
creator: Álvaro Corral
creator: Joan Serrà
creator: Martin Haro
creator: Perfecto Herrera
subject: MFCC
subject: large-scale data
subject: power-law
subject: sound retrieval
subject: timbre
author list: authorlist
abstract: Many sound-related applications use Mel-frequency cepstral coefficients (MFCC) to describe audio timbral content. Most of the research efforts dealing with MFCCs have been focused on the study of different classification and clustering algorithms, the use of complementary audio descriptors, or the effect of different distance measures. The goal of this paper is to focus on the statistical properties of the MFCC descriptor itself. For that purpose, we use a simple encoding process that maps a short-time MFCC vector to a dictionary of binary code-words. We study and characterize the rank-frequency distribution of such MFCC code-words, considering speech, music, and environmental sound sources. We show that, regardless of the sound source, MFCC code-words follow a shifted power-law distribution. This implies that there are a few code-words that occur very frequently and many that happen rarely, with no typical or characteristic code-word in the distribution. We also observe that the inner-structure of the most frequent code-words has characteristic patterns. For instance, we observe that close MFCC coefficients tend to have similar quantization values in the case of music signals. Finally, we study the rank-frequency distributions of individual music recordings and show that they present the same type of heavy-tailed distribution as found in the large-scale databases. This fact is exploited in two supervised semantic inference tasks: genre and instrument classification. In particular, we obtain similar classification results as the ones obtained by considering all frames in the recordings by just using 50 (properly selected) frames. Beyond this particular example, we believe that the fact that MFCC frames follow a power-law distribution could potentially have important implications for future applications dealing with audio signals.
year: 2012
type: InProceedings
maker: Álvaro Corral
maker: Joan Serrà
maker: Martin Haro
maker: Perfecto Herrera

Melody, Bassline and Harmony Representations for Music Version Identification
is part of: proceedings
creator: Emilia Gomez
creator: Joan Serrà
creator: Justin Salamon
subject: Version identification
subject: bassline
subject: cover song detection
subject: harmony
subject: melody extraction
subject: music retrieval
subject: music similarity
author list: authorlist
abstract: In this paper we compare the use of different musical representations for the task of version identification (i.e.~retrieving alternative performances of the same musical piece). We automatically compute descriptors representing the melody and bass line using a state-of-the-art melody extraction algorithm, and compare them to a harmony-based descriptor. The similarity of descriptor sequences is computed using a dynamic programming algorithm based on nonlinear time series analysis which has been successfully used for version identification with harmony descriptors. After evaluating the accuracy of individual descriptors, we assess whether performance can be improved by descriptor fusion, for which we apply a classification approach, comparing different classification algorithms. We show that both melody and bass line descriptors carry useful information for version identification, and that combining them increases version detection accuracy. Whilst harmony remains the most reliable musical representation for version identification, we demonstrate how in some cases performance can be improved by combining it with melody and bass line descriptions. Finally, we identify some of the limitations of the proposed descriptor fusion approach, and discuss directions for future research.
year: 2012
type: InProceedings
maker: Emilia Gomez
maker: Joan Serrà
maker: Justin Salamon

Adapting Similarity on the MagnaTagATune Dataset: Effects of Model and Feature Choices
is part of: proceedings
creator: Daniel Wolff
creator: Tillman Weyde
subject: context-based retrieval
subject: feature
subject: music information retrieval
subject: music similarity measures
subject: similarity adaptation
author list: authorlist
abstract: This work builds on and responds to previous publications on adaptation of similarity measures to user voting data from the MagnaTagATune dataset. The similarity dataset presented by Stober and Nürnberger at AMR 2011 has been reproduced to test other approaches in a comparable way. On this set, we compare their two-level approach, defining similarity measures on individual facets and combining them in a linear model, to the Metric Learning to Rank (MLR) algorithm which adapts a measure that operates directly on low-level features. We compare the different algorithms, features and parameter spaces with regards to minimising constraint violations. Furthermore, the effectiveness of the MLR algorithm in generalising over unknown similarity data is evaluated on this dataset. We explore the effects of feature choice. Here, we found that the binary genre data showed little correlation with the similarity data, but combined with audio features it clearly improved generalisation.
year: 2012
type: InProceedings
maker: Daniel Wolff
maker: Tillman Weyde

proceedings
has part: The Million Song Dataset Challenge
has part: Towards Minimal Test Collections for Evaluation of Audio Music Similarity and Retrieval
has part: Creating Large-Scale Searchable Digital Collections from Printed Music Materials
has part: Mining Microblogs to Infer Music Artist Similarity and Cultural Listening Patterns
has part: Combining Usage and Content in an Online Music Recommendation System for Music in the Long-Tail
has part: Music Retagging Using Label Propagation and Robust Principal Component Analysis
has part: Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals
has part: Melody, Bassline and Harmony Representations for Music Version Identification
has part: Adapting Similarity on the MagnaTagATune Dataset: Effects of Model and Feature Choices
related to event: AdMIRE 2012 : The Web of Music, 4th Workshop on Advances in Music Information Research
editor list: editor list
booktitle: Proceedings of 4th International Workshop on Advances in Music Information Research: "The Web of Music"
editor: Markus Schedl
editor: Oscar Celma
editor: Peter Knees
year: 2012
type: Proceedings

Programme chair
held by: Markus Schedl
held by: Oscar Celma
held by: Peter Knees
is role at: AdMIRE 2012 : The Web of Music, 4th Workshop on Advances in Music Information Research
type: Chair

Programme committee member
held by: Arjen De Vries
held by: Arthur Flexer
held by: Ben Fields
held by: Brian McFee
held by: Ching-Wei Chen
held by: Dmitry Bogdanov
held by: Dominik Schnitzer
held by: Emilia Gomez
held by: Fabien Gouyon
held by: Geraint Wiggins
held by: Ian Knopke
held by: Julián Urbano
held by: Jun Wang
held by: Malcolm Slaney
held by: Masataka Goto
held by: Mathieu Barthet
held by: Matthias Mauch
held by: Mehdi Elahi
held by: Mitsunori Ogihara
held by: Mohamed Sordo
held by: Noam Koenigstein
held by: Paul Lamere
held by: Robert Macrae
held by: Sebastian Stober
held by: Stephan Baumann
held by: Steve Tjoa
held by: Xiao Hu
is role at: AdMIRE 2012 : The Web of Music, 4th Workshop on Advances in Music Information Research
type: ProgrammeCommitteeMember

AdMIRE 2012 : The Web of Music, 4th Workshop on Advances in Music Information Research
has acronym: AdMIRE 2012
has related document: proceedings
complete graph: complete
has role: Programme chair
has role: Programme committee member
type: WorkshopEvent
url: http://www.cp.jku.at/conferences/admire2012/