A Simple Method to Determine if a Music Information Retrieval System is a “Horse”
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[1] A. Neuringer,et al. Music discriminations by pigeons. , 1984 .
[2] David J. Hand,et al. Deconstructing Statistical Questions , 1994 .
[3] R. Hamming. You Get What You Measure , 1997 .
[4] S. Watanabe,et al. Reinforcing property of music in Java sparrows (Padda oryzivora) , 1998, Behavioural Processes.
[5] A. R. Chase,et al. Music discriminations by carp (Cyprinus carpio) , 2001 .
[6] George Tzanetakis,et al. Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..
[7] François Pachet,et al. Representing Musical Genre: A State of the Art , 2003 .
[8] Simon Dixon,et al. Dance music classification: A tempo-based approach , 2004, ISMIR.
[9] François Pachet,et al. Improving Timbre Similarity : How high’s the sky ? , 2004 .
[10] Gerhard Widmer,et al. Evaluating Rhythmic descriptors for Musical Genre Classification , 2004 .
[11] Gerhard Widmer,et al. Improvements of Audio-Based Music Similarity and Genre Classificaton , 2005, ISMIR.
[12] N. Scaringella,et al. Automatic genre classification of music content: a survey , 2006, IEEE Signal Process. Mag..
[13] Geraint A. Wiggins,et al. How Many Beans Make Five? The Consensus Problem in Music-Genre Classification and a New Evaluation Method for Single-Genre Categorisation Systems , 2007, ISMIR.
[14] H. Yoshida. Tokyo, Japan , 2019, The Statesman’s Yearbook Companion.
[15] Petri Toiviainen,et al. MIR in Matlab (II): A Toolbox for Musical Feature Extraction from Audio , 2007, ISMIR.
[16] O. Lartillot,et al. A MATLAB TOOLBOX FOR MUSICAL FEATURE EXTRACTION FROM AUDIO , 2007 .
[17] François Pachet,et al. Signal + Context = Better Classification , 2007, ISMIR.
[18] Arthur Flexer,et al. A Closer Look on Artist Filters for Musical Genre Classification , 2007, ISMIR.
[19] Alastair J. D. Craft. The role of culture in music information retrieval : a model of negotiated musical meaning, and its implications in methodology and evaluation of the music genre classification task , 2008 .
[20] Mark Sandler,et al. Learning Latent Semantic Models for Music from Social Tags , 2008 .
[21] Thierry Bertin-Mahieux,et al. Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases , 2008 .
[22] R. A. Bailey,et al. Design of comparative experiments , 2008 .
[23] Gaël Richard,et al. Temporal Integration for Audio Classification With Application to Musical Instrument Classification , 2009, IEEE Transactions on Audio, Speech, and Language Processing.
[24] Geraint A. Wiggins. Semantic Gap?? Schemantic Schmap!! Methodological Considerations in the Scientific Study of Music , 2009, 2009 11th IEEE International Symposium on Multimedia.
[25] Jeffrey J. Scott,et al. State of the Art Report: Music Emotion Recognition: A State of the Art Review , 2010, ISMIR.
[26] Marcos Aurélio Domingues,et al. Three Current Issues In Music Autotagging , 2011, ISMIR.
[27] Fabien Gouyon,et al. Short-term Feature Space and Music Genre Classification , 2011 .
[28] Thierry Bertin-Mahieux,et al. Automatic Tagging of Audio: The State-of-the-Art , 2011 .
[29] Douglas Eck,et al. The need for music information retrieval with user-centered and multimodal strategies , 2011, MIRUM '11.
[30] Zhouyu Fu,et al. A Survey of Audio-Based Music Classification and Annotation , 2011, IEEE Transactions on Multimedia.
[31] Yading Song,et al. Evaluation of Musical Features for Emotion Classification , 2012, ISMIR.
[32] C. Lesimple,et al. Do Horses Expect Humans to Solve Their Problems? , 2012, Front. Psychology.
[33] Bob L. Sturm. A Survey of Evaluation in Music Genre Recognition , 2012, Adaptive Multimedia Retrieval.
[34] Luiz Eduardo Soares de Oliveira,et al. Music genre classification using LBP textural features , 2012, Signal Process..
[35] Jyh-Shing Roger Jang,et al. Discovering Time-Constrained Sequential Patterns for Music Genre Classification , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[36] Manuela M. Veloso,et al. Autonomous robot dancing driven by beats and emotions of music , 2012, AAMAS.
[37] Tomoko Matsui,et al. Music genre classification using self-taught learning via sparse coding , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[38] Bob L. Sturm. Classification accuracy is not enough , 2013, Journal of Intelligent Information Systems.
[39] Julián Urbano Merino,et al. Evaluation in audio music similarity , 2013 .
[40] Bob L. Sturm. Evaluating music emotion recognition: Lessons from music genre recognition? , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).
[41] Sebastian Ewert,et al. The Audio Degradation Toolbox and Its Application to Robustness Evaluation , 2013, ISMIR.
[42] Yann LeCun,et al. Feature learning and deep architectures: new directions for music informatics , 2013, Journal of Intelligent Information Systems.
[43] Xavier Serra,et al. Roadmap for Music Information ReSearch , 2013 .
[44] Bob L. Sturm. Making Explicit the Formalism Underlying Evaluation in Music Information Retrieval Research: A Look at the MIREX Automatic Mood Classification Task , 2013, CMMR.
[45] Markus Schedl,et al. The neglected user in music information retrieval research , 2013, Journal of Intelligent Information Systems.
[46] Edward R. Dougherty,et al. Scientific knowledge is possible with small-sample classification , 2013, EURASIP J. Bioinform. Syst. Biol..
[47] Bob L. Sturm. The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use , 2013, ArXiv.
[48] R. Paiva,et al. Multi-Modal Music Emotion Recognition: A New Dataset, Methodology and Comparative Analysis , 2013 .
[49] Xavier Serra,et al. Evaluation in Music Information Retrieval , 2013, Journal of Intelligent Information Systems.
[50] Bob L. Sturm. The State of the Art Ten Years After a State of the Art: Future Research in Music Information Retrieval , 2013, ArXiv.
[51] Bob L. Sturm,et al. A closer look at deep learning neural networks with low-level spectral periodicity features , 2014, 2014 4th International Workshop on Cognitive Information Processing (CIP).