메뉴 건너뛰기




Volumn 41, Issue 3, 2013, Pages 371-406

Classification accuracy is not enough: On the evaluation of music genre recognition systems

Author keywords

Classification; Evaluation; Genre; Music

Indexed keywords

CLASSIFICATION ACCURACY; EVALUATION; EXTRACT INFORMATIONS; GENRE; MUSIC; MUSIC INFORMATION RETRIEVAL; RECALL AND PRECISION; RECOGNITION SYSTEMS;

EID: 84888353738     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-013-0250-y     Document Type: Article
Times cited : (109)

References (132)
  • 1
    • 84866713590 scopus 로고    scopus 로고
    • Classification of music genres based on repetitive basslines
    • 10.1080/09298215.2011.641571
    • Abeßer, J., Lukashevich, H., Bräuer, P. (2012). Classification of music genres based on repetitive basslines. Journal of New Music Research, 41(3), 239-257.
    • (2012) Journal of New Music Research , vol.41 , Issue.3 , pp. 239-257
    • Abeßer, J.1    Lukashevich, H.2    Bräuer, P.3
  • 3
    • 84870518023 scopus 로고    scopus 로고
    • 4 The Facts on File, Inc New York
    • Ammer, C. (2004). Dictionary of music (4th ed.). New York: The Facts on File, Inc.
    • (2004) Dictionary of Music
    • Ammer, C.1
  • 5
    • 84885660005 scopus 로고    scopus 로고
    • Accessed 15 Oct 2012.
    • Andén, J., & Mallat, S. (2012). Scatterbox v. 1.02. http://www.cmap.polytechnique.fr/scattering/. Accessed 15 Oct 2012.
    • (2012) Scatterbox V. 1.02
    • Andén, J.1    Mallat, S.2
  • 6
    • 79955443392 scopus 로고    scopus 로고
    • Improving music genre classification using automatically induced harmony rules
    • 10.1080/09298215.2010.525654
    • Anglade, A., Benetos, E., Mauch, M., Dixon, S. (2010). Improving music genre classification using automatically induced harmony rules. Journal of New Music Research, 39(4), 349-361.
    • (2010) Journal of New Music Research , vol.39 , Issue.4 , pp. 349-361
    • Anglade, A.1    Benetos, E.2    Mauch, M.3    Dixon, S.4
  • 8
    • 78649431570 scopus 로고    scopus 로고
    • Sounds like teen spirit: Computational insights into the grounding of everyday musical terms
    • In J. Minett, & W. Wang (Eds.) Academia Sinica Press
    • Aucouturier, J.J. (2009). Sounds like teen spirit: Computational insights into the grounding of everyday musical terms. In J. Minett, & W. Wang (Eds.), Language, evolution and the brain: Frontiers in linguistic series (pp. 35-64). Academia Sinica Press.
    • (2009) Language, Evolution and the Brain: Frontiers in Linguistic Series , pp. 35-64
    • Aucouturier, J.J.1
  • 9
    • 84888352415 scopus 로고    scopus 로고
    • Seven problems that keep MIR from attracting the interest of cognition and neuroscience
    • doi: 10.1007/s10844-013-0251-x.
    • Aucouturier, J.-J. & Bigand, E. (2013). Seven problems that keep MIR from attracting the interest of cognition and neuroscience. Journal of Intelligent Information Systems. doi: 10.1007/s10844-013-0251-x.
    • (2013) Journal of Intelligent Information Systems
    • Aucouturier, J.-J.1    Bigand, E.2
  • 10
    • 2942747947 scopus 로고    scopus 로고
    • Representing music genre: A state of the art
    • 10.1076/jnmr.32.1.83.16801
    • Aucouturier, J.J., & Pachet, F. (2003). Representing music genre: A state of the art. Journal of New Music Research, 32(1), 83-93.
    • (2003) Journal of New Music Research , vol.32 , Issue.1 , pp. 83-93
    • Aucouturier, J.J.1    Pachet, F.2
  • 12
    • 57049152392 scopus 로고    scopus 로고
    • Introduction - From genres to tags: A little epistemology of music information retrieval research
    • 10.1080/09298210802479318
    • Aucouturier, J.J., & Pampalk, E. (2008). Introduction - from genres to tags: A little epistemology of music information retrieval research. Journal of New Music Research, 37(2), 87-92.
    • (2008) Journal of New Music Research , vol.37 , Issue.2 , pp. 87-92
    • Aucouturier, J.J.1    Pampalk, E.2
  • 13
    • 34547912516 scopus 로고    scopus 로고
    • Automatic classification of musical genres using inter-genre similarity
    • 10.1109/LSP.2006.891320
    • Baǧci, U., & Erzin, E. (2007). Automatic classification of musical genres using inter-genre similarity. IEEE Signal Processing Letters, 14(8), 521-524.
    • (2007) IEEE Signal Processing Letters , vol.14 , Issue.8 , pp. 521-524
    • Baǧci, U.1    Erzin, E.2
  • 15
    • 50249110559 scopus 로고    scopus 로고
    • Automatic musical genre classification using a flexible approach
    • Barbedo, J.G.A., & Lopes, A. (2008). Automatic musical genre classification using a flexible approach. Journal of the Audio Engineering Society, 56(7/8), 560-568.
    • (2008) Journal of the Audio Engineering Society , vol.56 , Issue.78 , pp. 560-568
    • Barbedo, J.G.A.1    Lopes, A.2
  • 18
    • 77956280275 scopus 로고    scopus 로고
    • Non-negative tensor factorization applied to music genre classification
    • 10.1109/TASL.2010.2040784
    • Benetos, E., & Kotropoulos, C. (2010). Non-negative tensor factorization applied to music genre classification. IEEE Transactions on Audio, Speech, and Language Processing, 18(8), 1955-1967.
    • (2010) IEEE Transactions on Audio, Speech, and Language Processing , vol.18 , Issue.8 , pp. 1955-1967
    • Benetos, E.1    Kotropoulos, C.2
  • 19
    • 2942735564 scopus 로고    scopus 로고
    • A large-scale evaluation of acoustic and subjective music-similarity measures
    • 10.1162/014892604323112257
    • Berenzweig, A., Logan, B., Ellis, D.P.W., Whitman, B. (2004). A large-scale evaluation of acoustic and subjective music-similarity measures. Computer Music Journal, 28(2), 63-76.
    • (2004) Computer Music Journal , vol.28 , Issue.2 , pp. 63-76
    • Berenzweig, A.1    Logan, B.2    Ellis, D.P.W.3    Whitman, B.4
  • 20
    • 33751531805 scopus 로고    scopus 로고
    • Aggregate features and AdaBoost for music classification
    • 10.1007/s10994-006-9019-7
    • Bergstra, J., Casagrande, N., Erhan, D., Eck, D., Kégl, B. (2006a). Aggregate features and AdaBoost for music classification. Machine Learning, 65(2-3), 473-484.
    • (2006) Machine Learning , vol.65 , Issue.2-3 , pp. 473-484
    • Bergstra, J.1    Casagrande, N.2    Erhan, D.3    Eck, D.4    Kégl, B.5
  • 25
    • 33645801332 scopus 로고    scopus 로고
    • Hierarchical automatic audio signal classification
    • Burred, J.J., & Lerch, A. (2004). Hierarchical automatic audio signal classification. Journal of the Audio Engineering Society, 52(7), 724-739.
    • (2004) Journal of the Audio Engineering Society , vol.52 , Issue.7 , pp. 724-739
    • Burred, J.J.1    Lerch, A.2
  • 28
    • 0035744629 scopus 로고    scopus 로고
    • Music discriminations by carp "cyprinus carpio"
    • Chase, A. (2001). Music discriminations by carp "Cyprinus carpio". Learning & Behavior, 29, 336-353.
    • (2001) Learning & Behavior , vol.29 , pp. 336-353
    • Chase, A.1
  • 30
    • 84873469306 scopus 로고    scopus 로고
    • Influence in early electronic dance music: An audio content analysis investigation
    • Collins, N. (2012). Influence in early electronic dance music: An audio content analysis investigation. In Proc. International Society for Music Information Retrieval (pp. 1-6).
    • (2012) Proc. International Society for Music Information Retrieval , pp. 1-6
    • Collins, N.1
  • 32
    • 55749112518 scopus 로고    scopus 로고
    • How many beans make five the consensus problem in music-genre classification and a new evaluation method for single-genre categorisation systems
    • Craft, A., Wiggins, G.A., Crawford, T. (2007). How many beans make five The consensus problem in music-genre classification and a new evaluation method for single-genre categorisation systems. In Proc. International Society for Music Information Retrieval (pp. 73-76).
    • (2007) Proc. International Society for Music Information Retrieval , pp. 73-76
    • Craft, A.1    Wiggins, G.A.2    Crawford, T.3
  • 33
    • 84944458232 scopus 로고    scopus 로고
    • Modeling musical style using grammatical inference techniques: A tool for classifying and generating melodies
    • doi: 10.1109/WDM.2003.1233878.
    • Cruz, P., & Vidal, E. (2003). Modeling musical style using grammatical inference techniques: a tool for classifying and generating melodies. In Proc. Web Delivering of Music (pp. 77-84). doi: 10.1109/WDM.2003.1233878.
    • (2003) Proc. Web Delivering of Music , pp. 77-84
    • Cruz, P.1    Vidal, E.2
  • 34
    • 40149106291 scopus 로고    scopus 로고
    • Two grammatical inference applications in music processing
    • 10.1080/08839510701853143
    • Cruz, P., & Vidal, E. (2008). Two grammatical inference applications in music processing. Applied Artificial Intelligence, 22(1/2), 53-76.
    • (2008) Applied Artificial Intelligence , vol.22 , Issue.12 , pp. 53-76
    • Cruz, P.1    Vidal, E.2
  • 36
    • 85085181752 scopus 로고    scopus 로고
    • Classification of music signals in the visual domain
    • Limerick, Ireland.
    • Deshpande, H., Singh, R., Nam, U. (2001). Classification of music signals in the visual domain. In Proc. Digital Audio Effects. Limerick, Ireland.
    • (2001) Proc. Digital Audio Effects
    • Deshpande, H.1    Singh, R.2    Nam, U.3
  • 39
    • 33645366175 scopus 로고
    • A theory of musical genres: Two applications
    • In P. Tagg & D. Horn (Eds.) Gothenburg and Exeter
    • Fabbri, F. (1982). A theory of musical genres: Two applications. In P. Tagg & D. Horn (Eds.), Popular music perspectives (pp. 55-59). Gothenburg and Exeter.
    • (1982) Popular Music Perspectives , pp. 55-59
    • Fabbri, F.1
  • 40
    • 33748450506 scopus 로고    scopus 로고
    • Statistical evaluation of music information retrieval experiments
    • 10.1080/09298210600834946
    • Flexer, A. (2006). Statistical evaluation of music information retrieval experiments. Journal of New Music Research, 35(2), 113-120.
    • (2006) Journal of New Music Research , vol.35 , Issue.2 , pp. 113-120
    • Flexer, A.1
  • 42
    • 84888367585 scopus 로고    scopus 로고
    • Album and artist effects for audio similarity at the scale of the web
    • Flexer, A., & Schnitzer, D. (2009). Album and artist effects for audio similarity at the scale of the web. In Proc. Sound and Music Computing (pp. 59-64).
    • (2009) Proc. Sound and Music Computing , pp. 59-64
    • Flexer, A.1    Schnitzer, D.2
  • 43
    • 77956580684 scopus 로고    scopus 로고
    • Effects of album and artist filters in audio similarity computed for very large music databases
    • 10.1162/COMJ-a-00004
    • Flexer, A., & Schnitzer, D. (2010). Effects of album and artist filters in audio similarity computed for very large music databases. Computer Music Journal, 34(3), 20-28.
    • (2010) Computer Music Journal , vol.34 , Issue.3 , pp. 20-28
    • Flexer, A.1    Schnitzer, D.2
  • 44
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • 1473055 10.1006/jcss.1997.1504
    • Freund, Y., & Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 45
    • 58849137028 scopus 로고    scopus 로고
    • Routledge New York
    • Frow, J. (2005). Genre. New York: Routledge.
    • (2005) Genre
    • Frow, J.1
  • 46
    • 79952972450 scopus 로고    scopus 로고
    • A survey of audio-based music classification and annotation
    • 10.1109/TMM.2010.2098858
    • Fu, Z., Lu, G., Ting, K.M., Zhang, D. (2011). A survey of audio-based music classification and annotation. IEEE Transactions on Multimedia, 13(2), 303-319.
    • (2011) IEEE Transactions on Multimedia , vol.13 , Issue.2 , pp. 303-319
    • Fu, Z.1    Lu, G.2    Ting, K.M.3    Zhang, D.4
  • 49
    • 57049173452 scopus 로고    scopus 로고
    • Scanning the dial: The rapid recognition of music genres
    • 10.1080/09298210802479268
    • Gjerdingen, R.O., & Perrott, D. (2008). Scanning the dial: The rapid recognition of music genres. Journal of New Music Research, 37(2), 93-100.
    • (2008) Journal of New Music Research , vol.37 , Issue.2 , pp. 93-100
    • Gjerdingen, R.O.1    Perrott, D.2
  • 53
    • 39649092019 scopus 로고    scopus 로고
    • Musical genre classification using nonnegative matrix factorization-based features
    • 10.1109/TASL.2007.909434
    • Holzapfel, A., & Stylianou, Y. (2008). Musical genre classification using nonnegative matrix factorization-based features. IEEE Transactions on Audio, Speech, and Language Processing, 16(2), 424-434.
    • (2008) IEEE Transactions on Audio, Speech, and Language Processing , vol.16 , Issue.2 , pp. 424-434
    • Holzapfel, A.1    Stylianou, Y.2
  • 55
    • 84866596498 scopus 로고    scopus 로고
    • Genre classification for million song dataset using confidence-based classifiers combination
    • Hu, Y., & Ogihara, M. (2012). Genre classification for million song dataset using confidence-based classifiers combination. In Proc. ACM Special Interest Group on Information Retrieval (pp. 1083-1084).
    • (2012) Proc. ACM Special Interest Group on Information Retrieval , pp. 1083-1084
    • Hu, Y.1    Ogihara, M.2
  • 56
    • 84888315556 scopus 로고    scopus 로고
    • Feature learning and deep architectures: New directions for music informatics
    • doi: 10.1007/s10844-013-0248-5.
    • Humphrey, E.J., Bello, J.P., LeCun, Y. (2013). Feature learning and deep architectures: New directions for music informatics. Journal of Intelligent Information Systems. doi: 10.1007/s10844-013-0248-5.
    • (2013) Journal of Intelligent Information Systems
    • Humphrey, E.J.1    Bello, J.P.2    Lecun, Y.3
  • 57
    • 84885655262 scopus 로고    scopus 로고
    • ISMIR Accessed 15 Oct 2012.
    • ISMIR (2004). Genre results. http://ismir2004.ismir.net/genre-contest/ index.htm. Accessed 15 Oct 2012.
    • (2004) Genre Results
  • 59
    • 70449561273 scopus 로고    scopus 로고
    • Automatic music genre classification using a hierarchical clustering and a language model approach
    • Langlois, T., & Marques, G. (2009). Automatic music genre classification using a hierarchical clustering and a language model approach. In Proc. International Conference on Advances in Multimedia (pp. 188-193).
    • (2009) Proc. International Conference on Advances in Multimedia , pp. 188-193
    • Langlois, T.1    Marques, G.2
  • 60
    • 84555194093 scopus 로고    scopus 로고
    • Human computation for music classification
    • T. Li M. Ogihara G. Tzanetakis (eds) CRC Press Boca Raton, FL 10.1201/b11041-13
    • Law, E. (2011). Human computation for music classification. In T. Li, M. Ogihara, G. Tzanetakis (Eds.), Music data mining (pp. 281-301). Boca Raton, FL: CRC Press.
    • (2011) Music Data Mining , pp. 281-301
    • Law, E.1
  • 61
    • 33745887470 scopus 로고    scopus 로고
    • Music genre classification using a time-delay neural network
    • In J. Wang, Z. Yi, J. Zurada, B.L. Lu, H. Yin (Eds.) Berlin/Heidelberg: Springer. doi: 10.1007/11760023-27.
    • Lee, J.W., Park, S.B., Kim, S.K. (2006). Music genre classification using a time-delay neural network. In J. Wang, Z. Yi, J. Zurada, B.L. Lu, H. Yin (Eds.), Advances in neural networks (pp. 178-187). Berlin/Heidelberg: Springer. doi: 10.1007/11760023-27.
    • (2006) Advances in Neural Networks , pp. 178-187
    • Lee, J.W.1    Park, S.B.2    Kim, S.K.3
  • 63
    • 13444256090 scopus 로고    scopus 로고
    • Music artist style identification by semi-supervised learning from both lyrics and contents
    • Li, T., & Ogihara, M. (2004). Music artist style identification by semi-supervised learning from both lyrics and contents. In Proc. ACM Multimedia (pp. 364-367).
    • (2004) Proc. ACM Multimedia , pp. 364-367
    • Li, T.1    Ogihara, M.2
  • 64
    • 84888369107 scopus 로고    scopus 로고
    • Music classification using significant repeating patterns
    • Y. Lee J. Li K.Y. Whang D. Lee (eds) Springer Berlin/Heidelberg
    • Lin, C.R., Liu, N.H., Wu, Y.H., Chen, A. (2004). Music classification using significant repeating patterns. In Y. Lee, J. Li, K.Y. Whang, D. Lee (Eds.), Database systems for advanced applications (pp. 27-29). Berlin/Heidelberg: Springer.
    • (2004) Database Systems for Advanced Applications , pp. 27-29
    • Lin, C.R.1    Liu, N.H.2    Wu, Y.H.3    Chen, A.4
  • 68
    • 84856508168 scopus 로고    scopus 로고
    • Genre identification of very brief musical excerpts
    • 10.1177/0305735610391347
    • Mace, S.T., Wagoner, C.L., Teachout, D.J., Hodges, D.A. (2011). Genre identification of very brief musical excerpts. Psychology of Music, 40(1), 112-128.
    • (2011) Psychology of Music , vol.40 , Issue.1 , pp. 112-128
    • Mace, S.T.1    Wagoner, C.L.2    Teachout, D.J.3    Hodges, D.A.4
  • 72
    • 84905173660 scopus 로고    scopus 로고
    • Additional evidence that common low-level features of individual audio frames are not representative of music genres
    • Marques, G., Lopes, M., Sordo, M., Langlois, T., Gouyon, F. (2010). Additional evidence that common low-level features of individual audio frames are not representative of music genres. In Proc. Sound and Music Computing.
    • (2010) Proc. Sound and Music Computing
    • Marques, G.1    Lopes, M.2    Sordo, M.3    Langlois, T.4    Gouyon, F.5
  • 74
    • 34447338051 scopus 로고    scopus 로고
    • Nonhuman primates prefer slow tempos but dislike music overall
    • 10.1016/j.cognition.2006.07.011 10.1016/j.cognition.2006.07.011
    • McDermott, J., & Hauser, M.D. (2007). Nonhuman primates prefer slow tempos but dislike music overall. Cognition, 104(3), 654-668. doi: 10.1016/j.cognition.2006.07.011.
    • (2007) Cognition , vol.104 , Issue.3 , pp. 654-668
    • McDermott, J.1    Hauser, M.D.2
  • 77
    • 84873429683 scopus 로고    scopus 로고
    • Music genre classification: Is it worth pursuing and how can it be improved in Proc
    • McKay, C., & Fujinaga, I. (2006). Music genre classification: Is it worth pursuing and how can it be improved In Proc. International Society for Music Information Retrieval (pp. 101-106).
    • (2006) International Society for Music Information Retrieval , pp. 101-106
    • McKay, C.1    Fujinaga, I.2
  • 79
    • 84873533162 scopus 로고    scopus 로고
    • An investigation of feature models for music genre classification using the support vector classifier
    • Meng, A., & Shawe-Taylor, J. (2008). An investigation of feature models for music genre classification using the support vector classifier. In Proc. International Society for Music Information Retrieval (pp. 604-609).
    • (2008) Proc. International Society for Music Information Retrieval , pp. 604-609
    • Meng, A.1    Shawe-Taylor, J.2
  • 80
    • 84888348586 scopus 로고    scopus 로고
    • MIREX (2005). Genre results Accessed 15 Oct 2012.
    • MIREX (2005). Genre results. http://www.music-ir.org/mirex/wiki/2005: MIREX2005-Results. Accessed 15 Oct 2012.
  • 81
    • 33749540712 scopus 로고    scopus 로고
    • Understandable models of music collections based on exhaustive feature generation with temporal statistics
    • Moerchen, F., Mierswa, I., Ultsch, A. (2006). Understandable models of music collections based on exhaustive feature generation with temporal statistics. In Int. Conference on Knowledge Discovery and Data Mining (pp. 882-891).
    • (2006) Int. Conference on Knowledge Discovery and Data Mining , pp. 882-891
    • Moerchen, F.1    Mierswa, I.2    Ultsch, A.3
  • 85
    • 84873668627 scopus 로고    scopus 로고
    • Music genre classification using locality preserving non-negative tensor factorization and sparse representations
    • Panagakis, Y., Kotropoulos, C., Arce, G.R. (2009a). Music genre classification using locality preserving non-negative tensor factorization and sparse representations. In Proc. International Society for Music Information Retrieval (pp. 249-254).
    • (2009) Proc. International Society for Music Information Retrieval , pp. 249-254
    • Panagakis, Y.1    Kotropoulos, C.2    Arce, G.R.3
  • 87
    • 76949097407 scopus 로고    scopus 로고
    • Non-negative multilinear principal component analysis of auditory temporal modulations for music genre classification
    • 10.1109/TASL.2009.2036813
    • Panagakis, Y., Kotropoulos, C., Arce, G.R. (2010a). Non-negative multilinear principal component analysis of auditory temporal modulations for music genre classification. IEEE Transactions on Audio, Speech, and Language Processing, 18(3), 576-588.
    • (2010) IEEE Transactions on Audio, Speech, and Language Processing , vol.18 , Issue.3 , pp. 576-588
    • Panagakis, Y.1    Kotropoulos, C.2    Arce, G.R.3
  • 88
    • 84863789031 scopus 로고    scopus 로고
    • Sparse multi-label linear embedding nonnegative tensor factorization for automatic music tagging
    • Panagakis, Y., Kotropoulos, C., Arce, G.R. (2010b). Sparse multi-label linear embedding nonnegative tensor factorization for automatic music tagging. In Proc. European Signal Processing Conference (pp. 492-496).
    • (2010) Proc. European Signal Processing Conference , pp. 492-496
    • Panagakis, Y.1    Kotropoulos, C.2    Arce, G.R.3
  • 89
    • 84888319549 scopus 로고    scopus 로고
    • Pfungst, O. (translated by C.L. Rahn) (1911). Clever hans (The horse of Mr. Von Osten): A contribution to experimental animal and human psychology. New York: Henry Holt.
    • Pfungst, O. (translated by C.L. Rahn) (1911). Clever hans (The horse of Mr. Von Osten): A contribution to experimental animal and human psychology. New York: Henry Holt.
  • 92
    • 84897583710 scopus 로고    scopus 로고
    • Discovering time-constrained sequential patterns for music genre classification
    • 10.1109/TASL.2011.2172426
    • Ren, J.M., & Jang, J.S.R. (2012). Discovering time-constrained sequential patterns for music genre classification. IEEE Transactions on Audio, Speech, and Language Processing, 20(4), 1134-1144.
    • (2012) IEEE Transactions on Audio, Speech, and Language Processing , vol.20 , Issue.4 , pp. 1134-1144
    • Ren, J.M.1    Jang, J.S.R.2
  • 94
    • 27144463192 scopus 로고    scopus 로고
    • On comparing classifiers: Pitfalls to avoid and a recommended approach
    • 10.1023/A:1009752403260
    • Salzberg, S.L. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1, 317-328.
    • (1997) Data Mining and Knowledge Discovery , vol.1 , pp. 317-328
    • Salzberg, S.L.1
  • 96
    • 84866723857 scopus 로고    scopus 로고
    • Algorithmic multi-genre classification of music: An empirical study
    • Sanden, C., & Zhang, J.Z. (2011a). Algorithmic multi-genre classification of music: An empirical study. In Proc. International Computer Music Conference (pp. 559-566).
    • (2011) Proc. International Computer Music Conference , pp. 559-566
    • Sanden, C.1    Zhang, J.Z.2
  • 98
    • 85032752479 scopus 로고    scopus 로고
    • Automatic genre classification of music content: A survey
    • 10.1109/MSP.2006.1598089
    • Scaringella, N., Zoia, G., Mlynek, D. (2006). Automatic genre classification of music content: A survey. IEEE Signal Processing Magazine, 23(2), 133-141.
    • (2006) IEEE Signal Processing Magazine , vol.23 , Issue.2 , pp. 133-141
    • Scaringella, N.1    Zoia, G.2    Mlynek, D.3
  • 99
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • 10.1023/A:1007614523901
    • Schapire, R., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3), 297-336.
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 297-336
    • Schapire, R.1    Singer, Y.2
  • 101
    • 84888379546 scopus 로고    scopus 로고
    • The neglected user in music information retrieval research
    • doi: 10.1007/s10844-013-0247-6.
    • Schedl, M., Flexer, A., Urbano, J. (2013). The neglected user in music information retrieval research. Journal of Intelligent Information Systems. doi: 10.1007/s10844-013-0247-6.
    • (2013) Journal of Intelligent Information Systems
    • Schedl, M.1    Flexer, A.2    Urbano, J.3
  • 103
    • 84988498579 scopus 로고    scopus 로고
    • Capturing the temporal domain in echonest features for improved classification effectiveness
    • Schindler, A., & Rauber, A. (2012). Capturing the temporal domain in echonest features for improved classification effectiveness. In Proc. Adaptive Multimedia Retrieval.
    • (2012) Proc. Adaptive Multimedia Retrieval.
    • Schindler, A.1    Rauber, A.2
  • 106
    • 84885652191 scopus 로고    scopus 로고
    • A comparison of human, automatic and collaborative music genre classification and user centric evaluation of genre classification systems
    • Seyerlehner, K., Widmer, G., Knees, P. (2010). A comparison of human, automatic and collaborative music genre classification and user centric evaluation of genre classification systems. In Proc. Adaptive Multimedia Retrieval (pp. 118-131).
    • (2010) Proc. Adaptive Multimedia Retrieval , pp. 118-131
    • Seyerlehner, K.1    Widmer, G.2    Knees, P.3
  • 111
    • 70349640319 scopus 로고    scopus 로고
    • Improved confidence intervals on the Bernoulli parameter
    • 2589793 10.1080/03610920802602958
    • Song, W., Chang, C.J., Liou, S. (2009). Improved confidence intervals on the Bernoulli parameter. Communications and Statistics Theory and Methods, 38(19), 3544-3560.
    • (2009) Communications and Statistics Theory and Methods , vol.38 , Issue.19 , pp. 3544-3560
    • Song, W.1    Chang, C.J.2    Liou, S.3
  • 116
    • 84885604808 scopus 로고    scopus 로고
    • On automatic music genre recognition by sparse representation classification using auditory temporal modulations
    • Sturm B.L., & Noorzad, P. (2012). On automatic music genre recognition by sparse representation classification using auditory temporal modulations. In Proc. Computer Music Modeling and Retrieval.
    • (2012) Proc. Computer Music Modeling and Retrieval.
    • Sturm, B.L.1    Noorzad, P.2
  • 117
    • 48149086379 scopus 로고    scopus 로고
    • Experiments in automatic genre classification of full-length music tracks using audio activity rate
    • Sundaram S., & Narayanan, S. (2007). Experiments in automatic genre classification of full-length music tracks using audio activity rate. In Proc. Workshop on Multimedia Signal Processing (pp. 98-102).
    • (2007) Proc. Workshop on Multimedia Signal Processing , pp. 98-102
    • Sundaram, S.1    Narayanan, S.2
  • 119
    • 85013709368 scopus 로고    scopus 로고
    • 4 Academic Press, Elsevier Amsterdam, The Netherlands
    • Theodoridis, S., & Koutroumbas, K. (2009). Pattern Recognition (4th ed.). Amsterdam, The Netherlands: Academic Press, Elsevier.
    • (2009) Pattern Recognition
    • Theodoridis, S.1    Koutroumbas, K.2
  • 122
    • 33750574645 scopus 로고    scopus 로고
    • Pitch histograms in audio and symbolic music information retrieval
    • 10.1076/jnmr.32.2.143.16743
    • Tzanetakis, G., Ermolinskyi, A., Cook, P. (2003). Pitch histograms in audio and symbolic music information retrieval. Journal of New Music Research, 32(2), 143-152.
    • (2003) Journal of New Music Research , vol.32 , Issue.2 , pp. 143-152
    • Tzanetakis, G.1    Ermolinskyi, A.2    Cook, P.3
  • 123
    • 16244420091 scopus 로고    scopus 로고
    • Multigroup classification of audio signals using time-frequency parameters
    • 10.1109/TMM.2005.843363
    • Umapathy, K., Krishnan, S., Jimaa, S. (2005). Multigroup classification of audio signals using time-frequency parameters. IEEE Transactions on Multimedia, 7(2), 308-315.
    • (2005) IEEE Transactions on Multimedia , vol.7 , Issue.2 , pp. 308-315
    • Umapathy, K.1    Krishnan, S.2    Jimaa, S.3
  • 125
    • 65649137930 scopus 로고    scopus 로고
    • Probing the Pareto frontier for basis pursuit solutions
    • 2466141 10.1137/080714488
    • van den Berg, E., & Friedlander, M.P. (2008). Probing the Pareto frontier for basis pursuit solutions. SIAM Journal on Scientific Computing, 31(2), 890-912.
    • (2008) SIAM Journal on Scientific Computing , vol.31 , Issue.2 , pp. 890-912
    • Van Den Berg, E.1    Friedlander, M.P.2
  • 126
    • 84890016602 scopus 로고    scopus 로고
    • Multi-objective evaluation of music classification
    • W.A. Gaul A. Geyer-Schulz L. Schmidt-Thieme J. Kunze (eds) Springer Berlin 10.1007/978-3-642-24466-7-41
    • Vatolkin, I. (2012). Multi-objective evaluation of music classification. In W.A. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, J. Kunze (Eds.), Challenges at the interface of data analysis, computer science, and optimization (pp. 401-410). Berlin: Springer.
    • (2012) Challenges at the Interface of Data Analysis, Computer Science, and Optimization , pp. 401-410
    • Vatolkin, I.1
  • 128
    • 0032848292 scopus 로고    scopus 로고
    • Discriminative stimulus properties of music in java sparrows
    • 1727534 10.1016/S0376-6357(99)00049-2
    • Watanabe, S., & Sato, K. (1999). Discriminative stimulus properties of music in java sparrows. Behavioural Processes, 47(1), 53-57.
    • (1999) Behavioural Processes , vol.47 , Issue.1 , pp. 53-57
    • Watanabe, S.1    Sato, K.2
  • 129
    • 77949513751 scopus 로고    scopus 로고
    • Semantic gap Schemantic schmap!! Methodological considerations in the scientific study of music
    • Wiggins, G.A. (2009). Semantic gap Schemantic schmap!! Methodological considerations in the scientific study of music. In Proc. IEEE International Symposium on Multimedia (pp. 477-482).
    • (2009) Proc. IEEE International Symposium on Multimedia , pp. 477-482
    • Wiggins . G, A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.