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The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR).
“Music genre classification using self-taught learning via sparse coding,”
K. Markov and T. Matsui, · 1932
Earlier work this paper cites.
Data mining and knowledge discovery
S. L. Salzberg, · 1997
Earlier work this paper cites.
“Auditory toolbox,”
M. Slaney, · 1998
Earlier work this paper cites.
“Musical genre classification of audio signals,”
G. Tzanetakis and P. Cook, · 2002
Earlier work this paper cites.
Manipulation, Analysis and Retrieval Systems for Audio Signals
G. Tzanetakis, · 2002
Earlier work this paper cites.
“A comparative study on content-based music genre classification,”
T. Li, M. Ogihara, and Q. Li, · 2003
Earlier work this paper cites.
“Factors in automatic musical genre classification of audio signals,”
T. Li and G. Tzanetakis, · 2003
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“An industrial strength audio search algorithm,”
A. Wang, · 2003
Earlier work this paper cites.
“Harmonicity and dynamics-based features for audio,”
H. Srinivasan and M. Kankanhalli, · 2004
Earlier work this paper cites.
Dictionary of Music
C. Ammer, · 2004
Earlier work this paper cites.
“Genre results,”
ISMIR, · 2004
Earlier work this paper cites.
“Genre classification via an LZ78-based string kernel,”
M. Li and R. Sleep, · 2005
Earlier work this paper cites.
“Music genre classification with taxonomy,”
T. Li and M. Ogihara, · 2005
Earlier work this paper cites.
“Evaluation of feature extractors and psycho-acoustic transformations for music genre classification,”
T. Lidy and A. Rauber, · 2005
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“On efficient music genre classification,”
J. Shen, J. Shepherd, and A. Ngu, · 2005
Earlier work this paper cites.
“Fast recognition of musical genres using RBF networks,”
D. Turnbull and C. Elkan, · 2005
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Turn the Beat Around: The Secret History of Disco
P. Shapiro, · 2005
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“Improvements of audio-based music similarity and genre classification,”
E. Pampalk, A. Flexer, and G. Widmer, · 2005
Earlier work this paper cites.
“Aggregate features and AdaBoost for music classification,”
J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. Kégl, · 2006
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“Algorithms for classifying recorded music by genre,”
J. Bergstra, · 2006
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“Music genres classification using text categorization method,”
K. Chen, S. Gao, Y. Zhu, and Q. Sun, · 2006
Earlier work this paper cites.
“Toward intelligent music information retrieval,”
T. Li and M. Ogihara, · 2006
Earlier work this paper cites.
“Evaluation of new audio features and their utilization in novel music retrieval applications,”
T. Lidy, · 2006
Earlier work this paper cites.
“Understandable models of music collections based on exhaustive feature generation with temporal statistics,”
F. Moerchen, I. Mierswa, and A. Ultsch, · 2006
Earlier work this paper cites.
“Towards effective content-based music retrieval with multiple acoustic feature combination,”
J. Shen, J. Shepherd, and A. H. H. Ngu, · 2006
Earlier work this paper cites.
“Audio music genre classification using different classifiers and feature selection methods,”
Y. Yaslan and Z. Cataltepe, · 2006
Earlier work this paper cites.
“Music genre classification: Is it worth pursuing and how can it be improved?,”
C. McKay and I. Fujinaga, · 2006
Earlier work this paper cites.
“Automatic classification of musical genres using inter-genre similarity,”
U. Bağci and E. Erzin, · 2007
Earlier work this paper cites.
“A statistical approach to musical genre classification using non-negative matrix factorization,”
A. Holzapfel and Y. Stylianou, · 2007
Earlier work this paper cites.
“Improving genre classification by combination of audio and symbolic descriptors using a transcription system,”
T. Lidy, A. Rauber, A. Pertusa, and J. M. I nesta, · 2007
Earlier work this paper cites.
“How many beans make five? The consensus problem in music-genre classification and a new evaluation method for single-genre categorisation systems,”
A. Craft, G. A. Wiggins, and T. Crawford, · 2007
Earlier work this paper cites.
“The role of culture in the music genre classification task: human behaviour and its effect on methodology and evaluation,”
A. Craft, · 2007
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“A closer look on artist filters for musical genre classification,”
A. Flexer, · 2007
Earlier work this paper cites.
“PR-Tools4.1, a matlab toolbox for pattern recognition,” Delft University of Technology, 2007,
R. P. W. Duin, P. Juszczak, D. de Ridder, P. Paclik, E. Pekalska, and D. M. J. Tax, · 2007
Earlier work this paper cites.
“A tensor-based approach for automatic music genre classification,”
E. Benetos and C. Kotropoulos, · 2008
Earlier work this paper cites.
“Relevance feedback in an adaptive space with one-class SVM for content-based music retrieval,”
G. Chen, T. Wang, and P. Herrera, · 2008
Earlier work this paper cites.
“Musical genre classification using nonnegative matrix factorization-based features,”
A. Holzapfel and Y. Stylianou, · 2008
Earlier work this paper cites.
“On the use of sparse time-relative auditory codes for music,”
P.-A Manzagol, T. Bertin-Mahieux, and D. Eck, · 2008
Earlier work this paper cites.
“Music genre classification: A multilinear approach,”
Y. Panagakis, E. Benetos, and C. Kotropoulos, · 2008
Earlier work this paper cites.
“Artificial immune system-based music genre classification,”
D. Sotiropoulos, A. Lampropoulos, and G. Tsihrintzis, · 2008
Earlier work this paper cites.
“Autotagger: A model for predicting social tags from acoustic features on large music databases,”
T. Bertin-Mahieux, D. Eck, F. Maillet, and P. Lamere, · 2008
Earlier work this paper cites.
“Combining feature kernels for semantic music retrieval,”
Luke Barrington, Mehrdad Yazdani, Douglas Turnbull, and Gert R. G. Lanckriet, · 2008
Earlier work this paper cites.
“Probing the Pareto frontier for basis pursuit solutions,”
E. van den Berg and M. P. Friedlander, · 2008
Earlier work this paper cites.
“Enhanced polyphonic music genre classification using high level features,”
A. F. Arabi and G. Lu, · 2009
Cited alongside, same era.
Audio content processing for automatic music genre classification: descriptors, databases, and classifiers
E. Guaus, · 2009
Cited alongside, same era.
“Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features,”
C. Lee, J. Shih, K. Yu, and H. Lin, · 2009
Cited alongside, same era.
“Enhancing timbre model using mfcc and its time derivatives for music similarity estimation,”
F. de Leon and K. Martinez, · 2009
Cited alongside, same era.
“Regional style automatic identification for Chinese folk songs,”
Y. Liu, L. Wei, and P. Wang, · 2009
Cited alongside, same era.
“Music genre classification via sparse representations of auditory temporal modulations,”
“Testing a spectral-based feature set for audio genre classification,”
M. A. Hartmann, · 2011
Later among the works it cites.
“Unsupervised learning of sparse features for scalable audio classification,”
M. Henaff, K. Jarrett, K. Kavukcuoglu, and Y. LeCun, · 2011
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“Automatic music genre classification using hybrid genetic algorithms,”
George V. Karkavitsas and George A. Tsihrintzis, · 2011
Later among the works it cites.
“Genre classification and the invariance of MFCC features to key and tempo,”
T. Li and A. Chan, · 2011
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“Music genre/mood classification using a feature-based modulation spectrum,”
S.-C. Lim, S.-J. Jang, S.-P. Lee, and M. Y. Kim, · 2011
Later among the works it cites.
“New trends in musical genre classification using optimum-path forest,”
C. Marques, I. R. Guiherme, R. Y. M. Nakamura, and J. P. Papa, · 2011
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Y. Panagakis, C. Kotropoulos, and G. R. Arce, · 2009
Cited alongside, same era.
“Music genre classification using locality preserving non-negative tensor factorization and sparse representations,”
Y. Panagakis, C. Kotropoulos, and G. R. Arce, · 2009
Cited alongside, same era.
“Audio genre classification by clustering percussive patterns,”
E. Tsunoo, G. Tzanetakis, N. Ono, and S. Sagayama, · 2009
Cited alongside, same era.
“Audio genre classification using percussive pattern clustering combined with timbral features,”
E. Tsunoo, G. Tzanetakis, N. Ono, and S. Sagayama, · 2009
Cited alongside, same era.
“A novel approach to musical genre classification using probabilistic latent semantic analysis model,”
Z. Zeng, S. Zhang, H. Li, W. Liang, and H. Zheng, · 2009
Cited alongside, same era.
“Semantic gap?? Schemantic schmap!! Methodological considerations in the scientific study of music,”
G. A. Wiggins, · 2009
Cited alongside, same era.
“Album and artist effects for audio similarity at the scale of the web,”
A. Flexer and D. Schnitzer, · 2009
Cited alongside, same era.
Later among the works it cites.
“Hierarchical audio classification using cepstral modulation ratio regressions based on legendre polynomials,”
A. Nagathil, P. Göttel, and R. Martin, · 2011
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“Time-constrained sequential pattern discovery for music genre classification,”
J.-M. Ren and J.-S. R. Jang, · 2011
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“Genre classification based on predominant melodic pitch contours,”
B. Rocha, · 2011
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“Higher-order moments for musical genre classification,”
J. S. Seo and S. Lee, · 2011
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“Nonlinear audio recurrence analysis with application to genre classification,”
J. Serra, C. A. de los Santos, and R. G. Andrzejak, · 2011
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“Beyond timbral statistics: Improving music classification using percussive patterns and bass lines,”
E. Tsunoo, G. Tzanetakis, N. Ono, and S. Sagayama, · 2011
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“Combining visual and acoustic features for music genre classification,”
M.-J. Wu, Z.-S. Chen, J.-S. R. Jang, and J.-M. Ren, · 2011
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“Deep belief networks for automatic music genre classification,”
X. Yang, Q. Chen, S. Zhou, and X. Wang, · 2011
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“A survey of audio-based music classification and annotation,”
Z. Fu, G. Lu, K. M. Ting, and D. Zhang, · 2011
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“Information retrieval meta-evaluation: Challenges and opportunities in the music domain,”
J. Urbano, · 2011
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“Human computation for music classification,”
E. Law, · 2011
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“A survey of evaluation in music genre recognition,”
B. L. Sturm, · 2012
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“A novel automatic hierachical approach to music genre classification,”
H. B. Ariyaratne and D. Zhang, · 2012
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“Image features in music style recognition,”
K. Behun, · 2012
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“Musical genre classification using ensemble of classifiers,”
D. Chathuranga and L. Jayaratne, · 2012
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“Optimization of an automatic music genre classification system via hyper-entities,”
G. V. Karkavitsas and F. A. Tsihrintzis, · 2012
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“Improved music similarity computation based on tone objects,”
J. Krasser, J. Abeßer, H. Großmann, C. Dittmar, and E. Cano, · 2012
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“Towards efficient music genre classification using FastMap,”
F. de Leon and K. Martinez, · 2012
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“Nonnegative matrix factorization based self-taught learning with application to music genre classification,”
K. Markov and T. Matsui, · 2012
Later among the works it cites.
“Discovering time-constrained sequential patterns for music genre classification,”
J.-M. Ren and J.-S. R. Jang, · 2012
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“Musical genre classification using melody features extracted from polyphonic music signals,”
J. Salamon, B. Rocha, and E. Gomez, · 2012
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“Capturing the temporal domain in echonest features for improved classification effectiveness,”
A. Schindler and A. Rauber, · 2012
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“From improved auto-taggers to improved music similarity measures,”
K. Seyerlehner, M. Schedl, R. Sonnleitner, D. Hauger, and B. Ionescu, · 2012
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“On automatic music genre recognition by sparse representation classification using auditory temporal modulations,”
B. L. Sturm and P. Noorzad, · 2012
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“Two systems for automatic music genre recognition: What are they really recognizing?,”
B. L. Sturm, · 2012
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“FPGA-based radio-on-demand broadcast receiver with musical genre identification,”
B. H. Tietche, O. Romain, B. Denby, L. Benaroya, and S. Viateur, · 2012
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“Unsupervised learning of local features for music classification,”
J. Wülfing and M. Riedmiller, · 2012
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“Supervised dictionary learning for music genre classification,”
C.-C. M. Yeh and Y.-H. Yang, · 2012
Later among the works it cites.
“Relevance feature mapping for content-based multimedia information retrieval,”
G.-T. Zhou, K. M. Ting, F. T. Liu, and Y. Yin, · 2012
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“An analysis of the GTZAN music genre dataset,”
B. L. Sturm, · 2012
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“Classification accuracy is not enough: On the evaluation of music genre recognition systems,”
B. L. Sturm, · 2013
Closest in time.
“On music genre classification via compressive sampling,”
B. L. Sturm, · 2013
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“Music genre recognition with risk and rejection,”
B. L. Sturm, · 2013
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“Dual-layer bag-of-frames model for music genre classification,”
C.-C. M. Yeh, L. Su, and Y.-H. Yang, · 2013
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“Evaluating music emotion recognition: Lessons from music genre recognition?,”
B. L. Sturm, · 2013
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“Comments on “automatic classification of musical genres using inter-genre similarity”,”
B. L. Sturm and F. Gouyon, · 2013
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