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Cross-Collection Evaluation For Music Classification Tasks

Dmitry Bogdanov, Alastair Porter, Perfecto Herrera, Xavier Serra
2016 Zenodo  
We also thank Gabriel Vigliensoni for help with mining tags.  ...  "Cross-collection evaluation for music classification tasks", 17th International Society for Music Information Retrieval Conference, 2016.  ...  In this paper we present a methodology and software tools for such evaluation for music classification tasks.  ... 
doi:10.5281/zenodo.1418130 fatcat:qf5oid6wi5am5j3ja72xqjjduu

Evaluation of state of the art for genre classification in large datasets

Vibhor Bajpai, Dmitry Bogdanov, Alastair Porter
2018 Zenodo  
The goal of this thesis is to evaluate state of the art methods for genre classification on some popular genre datasets and provide an alternate dataset for the music community to use for the task of genre  ...  Genre classification has been one of the main classification tasks in the MIR community due to its direct use in auto tagging of songs by the research community and the music industry alike.  ...  Cross collection evaluation The following table present the cross collection evaluation results for Jamendo, Rosamerica, and LastFM datasets.  ... 
doi:10.5281/zenodo.3770036 fatcat:ratzdk37nvaz7podnw37kayuei

Evaluation Of Feature Extractors And Psycho-Acoustic Transformations For Music Genre Classification

Thomas Lidy, Andreas Rauber
2005 Zenodo  
For a quantitive evaluation of each of the feature sets we measure their performance in classification tasks.  ...  Performance on all experiments was measured by the results in a music genre classification task.  ... 
doi:10.5281/zenodo.1416856 fatcat:wle6vwmejfau5klpvzujff7rji

The 2007 Mirex Audio Mood Classification Task: Lessons Learned

Xiao Hu 0001, J. Stephen Downie, Cyril Laurier, Mert Bay, Andreas F. Ehmann
2008 Zenodo  
Evaluation Method As in many other evaluations on classification tasks, the submitted systems were trained, tested and evaluated using cross-validation.  ...  As a starting point for evaluating on music mood classification, the AMC task was defined as a single-label classification problem, i.e., each song can only be classified into one mood cluster.  ... 
doi:10.5281/zenodo.1416380 fatcat:5k55tb6ykrhnhollzwvfe2utom

Emotion in Music Task at MediaEval 2014

Anna Aljanaki, Yi-Hsuan Yang, Mohammad Soleymani
2014 MediaEval Benchmarking Initiative for Multimedia Evaluation  
The Emotion in Music task addresses the task of automatic music emotion prediction and is held for the second year in 2014.  ...  In this paper we describe the dataset collection, annotations, and evaluation criteria, as well as the two required and optional runs.  ...  ACKNOWLEDGMENTS We are grateful to Sung-Yen Liu from Academia Sinica for helping with the task organization.  ... 
dblp:conf/mediaeval/AljanakiYS14 fatcat:2n2b3ottx5bmlhfz4sl3elgh2e

Generating ground truth for music mood classification using mechanical turk

Jin Ha Lee, Xiao Hu
2012 Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries - JCDL '12  
Specifically, we compare the mood classification judgments collected for the annual Music Information Retrieval Evaluation eXchange (MIREX) with judgments collected using MTurk.  ...  Mood is an important access point in music digital libraries and online music repositories, but generating ground truth for evaluating various music mood classification algorithms is a challenging problem  ...  ACKOWLEDGEMENTS We would like to thank the International Music Information Retrieval Systems Evaluation Lab (IMIRSEL) for providing us with access to the test collection for AMC task as well as past MIREX  ... 
doi:10.1145/2232817.2232842 dblp:conf/jcdl/LeeH12 fatcat:ypp6zqbnyzchvffnjr2xr4bl2e

S3T: Self-Supervised Pre-training with Swin Transformer for Music Classification [article]

Hang Zhao, Chen Zhang, Belei Zhu, Zejun Ma, Kejun Zhang
2022 arXiv   pre-print
We evaluate S3T on music genre classification and music tagging tasks with linear classifiers trained on learned representations.  ...  To our knowledge, S3T is the first method combining the Swin Transformer with a self-supervised learning method for music classification.  ...  We evaluate S3T on two music classification tasks, i.e., genre classification and music tagging with linear classifiers.  ... 
arXiv:2202.10139v1 fatcat:koku4zom2jeuxg5fzbidluagmm

MuLan: A Joint Embedding of Music Audio and Natural Language [article]

Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, Daniel P. W. Ellis
2022 arXiv   pre-print
We demonstrate the versatility of the MuLan embeddings with a range of experiments including transfer learning, zero-shot music tagging, language understanding in the music domain, and cross-modal retrieval  ...  Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries.  ...  To measure whether our proposed method deepens the text encoder's understanding of music related text, we directly evaluate the text embeddings with a triplet classification task.  ... 
arXiv:2208.12415v1 fatcat:2il6j7z5srhxbk7rtuyzlchqtm

Emotion Embedding Spaces for Matching Music to Stories

Minz Won, Justin Salamon, Nicholas J. Bryan, Gautham Mysore, Xavier Serra
2021 Zenodo  
., books), use multiple sentences as input queries, and automatically retrieve matching music. We formalize this task as a cross-modal text-to-music retrieval problem.  ...  Both the music and text domains have existing datasets with emotion labels, but mismatched emotion vocabularies prevent us from using mood or emotion annotations directly for matching.  ...  Recently, multiple music tagging models were evaluated in a homogeneous evaluation pipeline [27] and found three design recommendations for automatic music tagging models: (1) use mel-spectrogram inputs  ... 
doi:10.5281/zenodo.5624482 fatcat:uqlm3s5korb5rm2ybkbvr42qpi

Feature Selection Pitfalls And Music Classification

Rebecca Fiebrink, Ichiro Fujinaga
2006 Zenodo  
Acknowledgments We gratefully acknowledge support from the McGill University Max Stern Fellowship in Music and the Canada Foundation for Innovation.  ...  Extracting hundreds of features from a large music collection, however, is costly in terms of both time and space.  ...  That work used the evaluation methodology of [7] to show that feature selection was effective in improving classification accuracy on both musical and non-musical data.  ... 
doi:10.5281/zenodo.1415143 fatcat:nbq6ohxdpvfwxai7vmctkc4sey

Global Feature Versus Event Models For Folk Song Classification

Ruben Hillewaere, Bernard Manderick, Darrell Conklin
2009 Zenodo  
Special thanks are due to Xiao Li for correspondences on her original ABC corpus, to Daniel Müllensiefen for clarifications of the Fantastic feature set, to Mathieu Bergeron for assistance with the abc2midi  ...  code, and to Kerstin Neubarth, Stijn Meganck and Hélène Viratelle for helpful comments on a draft of this article.  ...  Table 3 . 3 Classification accuracies of the global feature sets on the Europa-6 collection, obtained by 10-fold cross validation.  ... 
doi:10.5281/zenodo.1417829 fatcat:xo3neb3hmferpgzfg3mfk33vgu

The Linked Data Mining Challenge 2016

Petar Ristoski, Heiko Paulheim, Vojtech Svátek, Vaclav Zeman
2016 Extended Semantic Web Conference  
This year's dataset collected music album ratings, where the task was to classify well and badly rated music albums.  ...  Acknowledgements We thank all participants for their interest in the challenge and their submissions.  ...  The proposed approach for album classification consists of three main steps. Given a collection of music albums, the authors first obtain the image of their cover art using DBpedia.  ... 
dblp:conf/esws/RistoskiPSZ16 fatcat:a6yhcd6fivgbzgvebcswhejvwi

On Rhythm And General Music Similarity

Tim Pohle, Dominik Schnitzer, Markus Schedl, Peter Knees, Gerhard Widmer
2009 Zenodo  
Setup for Rhythm Experiments We evaluate the rhythm descriptors on the ballroom dance music set 2 previously used by other authors, e.g. [5, 4, 2, 15, 7] and for the ISMIR'04 Dance Music Classification  ...  the task of general music similarity.  ... 
doi:10.5281/zenodo.1418229 fatcat:txz62udoqrgj7cw4j5w3xpzhki

TensorFlow Audio Models in Essentia [article]

Pablo Alonso-Jiménez, Dmitry Bogdanov, Jordi Pons, Xavier Serra
2020 arXiv   pre-print
Essentia is a reference open-source C++/Python library for audio and music analysis.  ...  In particular, we assess the generalization capabilities in a cross-collection evaluation utilizing both external tag datasets as well as manual annotations tailored to the taxonomies of our models.  ...  For this reason, we conduct a cross-collection evaluation that consists in evaluating the models on an independent source of music and annotations following the methodology proposed in [15] .  ... 
arXiv:2003.07393v1 fatcat:4umpevhss5arxnma333v3sypdu

Homogeneous segmentation and classifier ensemble for audio tag annotation and retrieval

Hung-Yi Lo, Ju-Chiang Wang, Hsin-Min Wang
2010 2010 IEEE International Conference on Multimedia and Expo  
For the audio retrieval task, we propose using ranking ensemble.  ...  We participated in the MIREX 2009 audio tag classification task and our system was ranked first in terms of F-measure and the area under the ROC curve given a tag.  ...  Given a music clip, we hope the tagging algorithm can automatically predict tags for the music clip based on the models trained from music clips with associated tags collected beforehand.  ... 
doi:10.1109/icme.2010.5583009 dblp:conf/icmcs/LoWW10 fatcat:tutfvsbij5bkzjvbnzs5gco7se
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