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Assessing Algorithmic Biases for Musical Version Identification [article]

Furkan Yesiler and Marius Miron and Joan Serrà and Emilia Gómez
2021 arXiv   pre-print
Version identification (VI) systems now offer accurate and scalable solutions for detecting different renditions of a musical composition, allowing the use of these systems in industrial applications and  ...  We find signs of disparities in identification performance for most of the groups we include in our analyses.  ...  However, quantifying algorithmic biases is a useful step for mitigating potential unfair decisions.  ... 
arXiv:2109.15188v1 fatcat:4drrscwbcncszby4tuxx3vv34e

Melody, bass line, and harmony representations for music version identification

Justin Salamon, Joan Serrà, Emilia Gómez
2012 Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion  
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).  ...  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  ...  steps for obtaining useful descriptors for version identification.  ... 
doi:10.1145/2187980.2188219 dblp:conf/www/SalamonSG12 fatcat:zfqixcy3svdtpiynhdzpn2jtq4

Tonal representations for music retrieval: from version identification to query-by-humming

Justin Salamon, Joan Serrà, Emilia Gómez
2012 International Journal of Multimedia Information Retrieval  
In this study we compare the use of different music representations for retrieving alternative performances of the same musical piece, a task commonly referred to as version identification.  ...  Our results show that whilst harmony is the most reliable music representation for version identification, the melody and bass line representations also carry useful information for this task.  ...  They would also like to thank the reviewers for their valuable feedback and suggestions for improvement.  ... 
doi:10.1007/s13735-012-0026-0 fatcat:stw6zfzexne7fncqhoj6p6mmmy

Cross recurrence quantification for cover song identification

Joan Serrà, Xavier Serra, Ralph G Andrzejak
2009 New Journal of Physics  
In particular, we address the automatic identification of cover songs, i.e. alternative renditions of a previously recorded musical piece.  ...  For this purpose we here propose a recurrence quantification analysis measure that allows tracking potentially curved and disrupted traces in cross recurrence plots.  ...  Herrera for useful discussions and M. Koppenberger for technical support.  ... 
doi:10.1088/1367-2630/11/9/093017 fatcat:gjom3d2ylrgtxdmkbi5gpl2ane

The Dilemma of False Positives: Making Content ID Algorithms more Conducive to Fostering Innovative Fair Use in Music Creation

Toni Lester, Dessislava Pachamanova
2017 UCLA Entertainment Law Review  
The proposed regulatory scheme should lead to a more equitable system for music creators and original copyright holders and to more inventive and interesting music for fans.  ...  Content ID programs commonly use algorithms to block uploaded music when the algorithm concludes the owners of certain copyrighted works will claim their work is being used without consent.  ...  I PAID FOR THE DARN TRACKS and many were posted well before the musician went and made their version. I AM NOT using the mixed musician's version.  ... 
doi:10.5070/lr8241035525 fatcat:p5atjw5nibbztleb66odv3bxp4

Music similarity based on sequences of descriptors tonal features applied to audio cover song identification

Joan Serrà, Xavier Serra
2007 Zenodo  
, key transposition methods), apart from assessing the intrinsic algorithms' parameters.  ...  We do that while focusing to an application that have been increasing his popularity along these last few years, as it provides a direct and objective way for evaluating music similarity cover song identification  ...  For assessing the performance of a cover song identification system, an evaluation methodology has to be established.  ... 
doi:10.5281/zenodo.3743173 fatcat:n4owz6prnzh4tdzr4jqttjinbe

Community-Based Cover Song Detection

Jonathan Donier
2019 Zenodo  
Instead of considering pairs of tracks in isolation, we consider larger sets of potential versions for a given composition, and create and exploit the graph of relationships between these tracks.  ...  We show that this can result in a significant improvement in performance, in particular when the number of existing versions of a given composition is large.  ...  This algorithm has been refined in [8] by adding a few modifications such as tempo biasing.  ... 
doi:10.5281/zenodo.3527789 fatcat:icup6nhx2bhtxmzmxs66stcx4a

A Comparison of Random Forests and Ferns on Recognition of Instruments in Jazz Recordings [article]

Alicja A. Wieczorkowska, Miron B. Kursa
2013 arXiv   pre-print
In this paper, we first apply random ferns for classification of real music recordings of a jazz band.  ...  The notion of random ferns is described in the paper, to familiarize the reader with this classification algorithm, which was introduced quite recently and applied so far in image recognition tasks.  ...  Elżbieta Kubera from the University of Life Sciences in Lublin for preparing the ground-truth data for initial experiments, and Rados law Rudnicki from the University of York for preparing the jazz band  ... 
arXiv:1305.5078v1 fatcat:4jdgdmaohzfpzd7vogdoarv7tq

Audio-based Musical Version Identification: Elements and Challenges [article]

Furkan Yesiler, Guillaume Doras, Rachel M. Bittner, Christopher J. Tralie, Joan Serrà
2021 arXiv   pre-print
In this article, we aim to provide a review of the key ideas and approaches proposed in 20 years of scientific literature around musical version identification (VI) research and connect them to current  ...  For more than a decade, VI systems suffered from the accuracy-scalability trade-off, with attempts to increase accuracy that typically resulted in cumbersome, non-scalable systems.  ...  Therefore, solving problems other than identification performance may be crucial for a VI system to be used for setlist identification.  ... 
arXiv:2109.02472v1 fatcat:tbbd66yq2vcz3ahc4z5ymethgi

Evaluating an Analysis-by-Synthesis Model for Jazz Improvisation

Klaus Frieler, Wolf-Georg Zaddach
2022 Transactions of the International Society for Music Information Retrieval  
Accordingly, we found indications that assessments of origin of a solo are partly driven by aesthetic judgments.  ...  First, we present a generative model for (monophonic) jazz improvisation whose main purpose is testing hypotheses on creative processes during jazz improvisation.  ...  ACKNOWLEDGEMENTS We like to thank all participants in the evaluation experiment, Simon Dixon for proof-reading the manuscript, and three anomymous reviewers for their helpful comments.  ... 
doi:10.5334/tismir.87 fatcat:mzbvcsfszvbprjyglrzyw4kxxq

Addressing Time Bias in Bipartite Graph Ranking for Important Node Identification [article]

Hao Liao, Jiao Wu, Mingyang Zhou, Alexandre Vidmer
2019 arXiv   pre-print
The problem of ranking the nodes in bipartite networks is valuable for many real-world applications.  ...  However, many classical ranking algorithms share a common drawback: they tend to rank older movies higher than newer movies, though some newer movies may have a high quality.  ...  Time biased ranking algorithm We design our framework so that any ranking algorithm can be used as a baseline method to obtain the scores.  ... 
arXiv:1911.12558v1 fatcat:2poyxmyw2zbbzi2dx2546e6pui

Improving Multilabel Analysis of Music Titles: A Large-Scale Validation of the Correction Approach

F. Pachet, P. Roy
2009 IEEE Transactions on Audio, Speech, and Language Processing  
, PathBuilder, intelligent music scheduling using metadata, The Continuator for Interactive Music Improvization).  ...  He then set up the music research team at SONY Computer Science Laboratory, Paris, and developed the vision that metadata can greatly enhance the musical experience in all its dimensions, from listening  ...  Aucouturier designed and implemented a preliminary version of the correction approach. R.  ... 
doi:10.1109/tasl.2008.2008734 fatcat:sx2dp3i2hbh5jc7vbipr27zypq

ADTOF: A large dataset of non-synthetic music for automatic drum transcription

Mickael Zehren, Marco Alunno, Paolo Bientinesi
2021 Zenodo  
This dataset contains real-world music, is manually annotated, and is about two orders of magnitude larger than any other non-synthetic dataset, making it a prime candidate for training purposes.  ...  The problem is that the available public datasets are limited either in size or in realism, and are thus suboptimal for training purposes.  ...  Lastly, in these datasets, data diversity is largely reduced (e.g., ENST contains audio from a limited number of drum kits, RBMA is biased toward a few music genres).  ... 
doi:10.5281/zenodo.5624526 fatcat:nz2wzqz2bbamxiy4ldfex4vjp4

Note Onset Deviations as Musical Piece Signatures

Joan Serrà, Tan Hakan Özaslan, Josep Lluis Arcos, Derek Abbott
2013 PLoS ONE  
Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions.  ...  Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research  ...  or jazz versions mentioned above).  ... 
doi:10.1371/journal.pone.0069268 pmid:23935971 pmcid:PMC3729570 fatcat:xkrzhvhr5zhojmpmawzxgxj2ua

Early Mfcc And Hpcp Fusion For Robust Cover Song Identification

Christopher J. Tralie
2017 Zenodo  
Our current best remedy for this is to use different tempo biases, which blows up computation by a factor of 9.  ...  In the process, we also developed a novel cross-similarity fusion scheme which was validated on several datasets, and which we believe could be useful beyond cover song identification in music structure  ... 
doi:10.5281/zenodo.1417331 fatcat:hal2s2pjxrdhzj6xjfwuhnpyyy
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