research-article

A survey on symbolic data-based music genre classification

Authors:

Débora C. Corrêa,

Francisco Ap. RodriguesAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 60, Issue C

Pages 190 - 210

https://doi.org/10.1016/j.eswa.2016.04.008

Published: 30 October 2016 Publication History

Abstract

Music is present in everyday life and used for a wide range of objectives. Musical databases have considerably increased in number and size over the past years, therefore, the development of accurate tools for music information retrieval (MIR) has become an important topic in computer science. The increasing theoretical advances in machine learning algorithms together with the abundance of recordings available in digital audio formats, the growing quality and accessibility of on-line symbolic music data, and availability of tools and toolboxes for the extraction of musical properties have motivated many studies on machine learning and MIR. Relevant problems in MIR include classification of songs into genres, which enables the summarization of common features (or patterns) shared by different songs. The automatic classification of music genres plays a fundamental role in the context of music indexing and retrieval, so that websites and device music engines can manage and label music content. Most studies have dealt with such an issue by extracting music characteristics from the audio content, and some have provided overviews of audio features and classification algorithms for music genre classification. However, precise high-level musical information can be extracted from symbolic data (e.g. digital music scores), known to be closely related to the way humans perceive music. A number of approaches use such musical information to process, retrieve and classify music content. This manuscript provides an overview of the most important approaches that deal with music genre classification and consider the symbolic representation of music data. Current issues inherent to such a music format, as well the main algorithms adopted for the modeling of the music feature space are presented.

References

[1]

J. Abeßer, P. Bräuer, H. Lukashevich, G. Schuller, Bass playing style detection based on high-level features and pattern similarity, 2010.

[2]

J. Abeßer, C. Dittmar, H. Grossmann, Automatic genre and artist classification by analyzing improvised solo parts from musical recordings, 2008.

[3]

J. Abeßer, H. Lukashevich, P. Bräuer, Classification of music genres based on repetitive basslines, Journal of New Music Research, 41 (2012) 239-257.

[4]

J. Abeßer, H. Lukashevich, C. Dittmar, G. Schuller, Genre classification using bass-related high-level features and playing styles, 2009.

[5]

ACE (2005). Ace 2.0. http://jmir.sourceforge.net/index_ACE.html.

[6]

D.R. Amancio, C.H. Comin, D. Casanova, G. Travieso, O.M. Bruno, F.A. Rodrigues, L.d.F. Costa, A systematic comparison of supervised classifiers, PLoS ONE, 9 (2014) e94137.

[7]

Y. Anan, K. Hatano, H. Bannai, M. Takeda, Music genre classification using similarity functions, University of Miami, Miami, FL, 2011.

[8]

A. Anglade, E. Benetos, M. Mauch, S. Dixon, Improving music genre classification using automatically induced harmony rules, Journal of New Music Research, 39 (2010) 349-361.

[9]

A. Anglade, R. Ramirez, S. Dixon, First-order logic classification models of musical genres based on harmony, 2009.

[10]

A. Anglade, R. Ramirez, S. Dixon, Genre clasification using harmony rules induced from automatic chord transcriptions, 2009.

[11]

J.-J. Aucouturier, F. Pachet, Representing musical genre: A state of the art, Journal of New Music Research, 32 (2003) 83-93.

[12]

R. Basili, A. Serafini, A. Stellato, Classification of musical genre: a machine learning approach, 2004.

[13]

D. Bobdanov, J. Serrá, N. Wack, P. Herrera, X. Serra, Unifying low-level and high-level music similarity measures, IEEE Transactions on Multimedia, 13 (2011) 687-701.

Digital Library

[14]

B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, 1992.

[15]

M.A. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, M. Slaney, Content-based information retrieval: Current directions and future challenges, Proceedings of the IEEE, 96 (2008) 668-696.

[16]

Z. Cataltepe, Y. Yaslan, A. Sonmez, Music genre classification using MIDI and audio features, EURASIP Journal on Advances in Signal Processing, 2007 (2007) 1-8.

Digital Library

[17]

W. Chai, B. Vercoe, Folk music classification using hidden markov models, 2001.

[18]

R. Cilibrasi, P. Vitányi, R. de Wolf, Algorithmic clustering of music, 2004.

[19]

R. Cilibrasi, P. Vitányi, R. de Wolf, Algorithmic clustering of music based on string compression, Computer Music Journal, 28 (2004) 49-67.

Digital Library

[20]

D. Conklin, Melodic analysis with segment classes, Machine Learning, 65 (2006) 349-360.

Digital Library

[21]

D. Conklin, Melody classification using patterns, 2009.

[22]

D. Conklin, Multiple viewpoint systems for music classification, Journal of New Music Research, 42 (2013) 19-26.

[23]

D.C. Corrêa, L.d.F. Costa, A.L.M. Levada, Finding community structure in music genre networks, University of Miami, Miami, FL, 2011.

[24]

D.C. Corrêa, L.d.F. Costa, J.H. Saito, Using digraphs and a second-order Markovian model for rhythm classification, in: Communications in computer and information science., Vol. 116, Springer, Berlin, Heidelberg, 2011, pp. 85-95.

[25]

D.C. Corrêa, J.H. Saito, L.d.F. Costa, Musical genres: beating to the rhythms of different drums., New Journal of Physics, 12 (2010) 1-37.

[26]

L.d.F. Costa, R.M. César, CRC Press, FL, USA, 2001.

[27]

N. Cristianini, J. Shawe-Taylor, Cambridge University Press, Cambridge, UK, 2000.

[28]

P.P. Cruz-Alcázar, E. Vidal-Ruiz, Learning regular grammars to model musical style: Comparing different coding schemes, in: Lecture notes on computer science, Vol. 1433, Springer-Verlag, Berlin, Heidelberg, 1998, pp. 211-222.

Digital Library

[29]

P.P. Cruz-Alcázar, E. Vidal-Ruiz, Modeling musical style using grammatical inference techniques: a tool for classifying and generating melodies, 2003.

[30]

P.P. Cruz-Alcázar, E. Vidal-Ruiz, J.C. Pérez-Cortés, Musical style identification using grammatical inference: the encoding problem, in: Lecture notes on computer science., Vol. 2905, Springer-Verlag, Berlin, Heidelberg, 2003, pp. 375-382.

[31]

Cuthbert, M. S., Ariza, C., & Friedland, L. (a). music21: a toolkit for computer-aided musicology. web.mit.edu/music21/.

[32]

M.S. Cuthbert, C. Ariza, L. Friedland, Feature extraction and machine learning on symbolic music using the music21 toolkit, University of Miami, Miami, FL, 2011.

[33]

R.B. Dannenberg, B. Thom, D. Watson, A machine learning approach to musical style recognition, 1997.

[34]

C. DeCoro, Z. Barutcuoglu, R. Fiebrink, Bayesian agrregation for hierarchical genre classification, 2007.

[35]

P.J. de León, J.M. Iñesta, Musical style identification using self-organizing maps, 2002.

[36]

P.J. de León, J.M. Iñesta, Statistical description models for melody analysis and characterization, 2004.

[37]

de León, P. J. P. (c). A statistical pattern recognition approach to symbolic music classification (Ph.D. thesis). Universidad de Alicante.

[38]

P.J.P. de León, J.M. Iñesta, Feature-driven recognition of music sytles, in: Lecture notes on computer science, Vol. 2652, Springer, Berlin, Heidelberg, 2003, pp. 773-781.

[39]

P.J.P. de León, J.M. Iñesta, Musical style classification from symbolic data: a two-styles case study, in: Lecture notes on computer science, Vol. 2771, Springer, Berlin, Heidelberg, 2004, pp. 167-178.

[40]

P.J.P. de León, J.M. Iñesta, Pattern recognition approach for music style identification using shallow statistical descriptors, IEEE Transactions on Systems, Man and Cybernetics, 37 (2007) 248-257.

Digital Library

[41]

P.J.P. de León, J.M. Iñesta, C. Pérez-Sancho, Classifier ensembles for genre recognition, in: Pattern recognition: Progress, directions and applications, Centre de Visió per Computador - Universitat Autónoma de Barcelona, 2006, pp. 41-53.

[42]

P.J.P. de León, J.M. Iñesta, D. Rizo, Mining digital music score collections: melody extraction and genre recognition, in: Pattern recognition techniques, technology and applications, I-Tech, Vienna, Austria, 2008, pp. 559-590.

[43]

P.J.P. de León, C. Pérez-Sancho, J.M. Iñesta, A shallow description framework for musical style recognition, in: Lecture notes on computer science, Vol. 3138, Springer, Berlin, Heidelberg, 2004, pp. 876-884.

[44]

R.O. Duda, P.E. Hart, D.G. Stork, John Wiley & Sons Inc, New York, 2001.

[45]

T. Eerola, P. Toiviainen, Mir in matlab: The midi toolbox, 2004.

[46]

Eerola, T., & Toiviainen, P. (2006). Midi toolbox. https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/miditoolbox/.

[47]

D.P.W. Ellis, J. Arroyo, Eigenrhythms:drum pattern basis sets for classification and generation, 2004.

[48]

Fan (2009). Modelling melodic memory and the perception of melodic similarity. http://doc.gold.ac.uk/isms/mmm.

[49]

Z. Fu, G. Lu, K.M. Ting, D. Zhang, A survey of audio-based music classification and annotation, IEEE Transactions on Multimedia, 13 (2011) 303-319.

Digital Library

[50]

D. Gamerman, H.F. Lopes, Chapman and Hall/CRC, 2006.

[51]

G. Gan, C. Ma, J. Wu, SIAM: Society for Industrial and Applied Mathematics, Alexandria, Virginia, 2007.

[52]

R.O. Gjerdingen, D. Perrott, Scanning the dial: The rapid recognition of music genres, Journal of New Music Research, 37 (2008) 93-100.

[53]

M. Goto, T. Nishimura, H. Hashiguchi, R. Oka, Rwc music database: Popular, classical, and jazz music databases, 2002.

[54]

F. Gouyon, S. Dixon, A review of automatic rhythm description systems, Computer Music Journal, 29 (2005) 34-54.

Digital Library

[55]

Haynes, B., & Cooke, P. (b). Grove music online. http://www.oxfordmusiconline.com/subscriber/book/omo_gmo.

[56]

T. Hedges, P. Roy, F. Pachet, Predicting the composer and style of jazz chord progressions, Journal of New Music Research, 43 (2014) 276-290.

[57]

R. Hillewaere, B. Manderick, D. Conklin, Global feature versus event models for folk song classification, 2009.

[58]

R. Hillewaere, B. Manderick, D. Conklin, String methods for folk tune genre classification, 2012.

[59]

R. Hillewaere, B. Manderick, D. Conklin, Alignment methods for folk tune classification, Springer, 2014.

[60]

H. Homburg, I. Mierswa, B. Möller, K. Morik, M. Wurst, A benchmark dataset for audio classification and clustering, University of London, London, UK, 2005.

[61]

J.-L. Hsu, C.-C. Liu, C.-y. Chen, Discovering non-trivial repeating patterns in music data, IEEE Transactions on Multimedia, 3 (2001) 311-325.

Digital Library

[62]

S. Hübler, R. Hoffmann, Modelling drum patterns with weighted finite-state transducers, Institute of Electrical and Electronics Engineers, Inc, Vancouver, Canada, 2013.

[63]

B. Jesser, Peter Lang, Bern, Switzerland, 1991.

[64]

Z. Juhász, A systematic comparison of different european folk music traditions using self-organizing maps, Journal of New Music Research, 35 (2006) 95-112.

[65]

I. Karydis, Symbolic music genre classification based on note pitch and duration, in: Lecture notes on computer science, Vol. 4152, Springer-Verlag, Berlin, Heidelberg, 2006, pp. 329-338.

Digital Library

[66]

I. Karydis, A. Nanopoulos, Y. Manolopoulos, Symbolic musical genre classification based on repeating patterns, 2006.

[67]

S. Khoo, Z. Man, Z. Cao, Automatic han chinese folk song classification using the musical feature density map, Institute of Electrical and Electronics Engineers (IEEE), Gold Coast, QLD, 2012.

[68]

C. Kofod, D. Ortiz-Arroyo, Exploring the design space of symbolic music genre classification using data mining techniques, IEEE Computer Society, Viena, Austria, 2008.

[69]

A. Kotsifakos, E.E. Kotsifakos, P. Papapetrou, V. Athitsos, Genre classification of symbolic music with SMBGT, 2013.

[70]

J.H. Lee, J.S. Downie, Survey of music information needs, uses, and seeking behaviours: Preliminary findings, 2004.

[71]

M. Li, X. Chen, X. Li, B. Ma, P.M.B. Vitányi, The similarity metric, IEEE Transactions on Information Theory, 50 (2004) 3250-3264.

Digital Library

[72]

M. Li, R. Sleep, Improving melody classification by discriminant feature extraction and fusion, 2004.

[73]

M. Li, R. Sleep, Melody classification using a similarity metric based on Kolmogorov complexity, 2004.

[74]

X. Li, G. Ji, J. Bilmes, A factored language model of quantized pitch and duration, 2006.

[75]

T. Lidy, A. Rauber, A. Pertusa, J.M. Iñesta, Improving genre classification by combination of audio and symbolic descriptors using a transcription system, 2007.

[76]

B.-S. Lin, T.-C. Chen, Genre classification for musical documents based on extracted melodic patterns and clustering, 2012.

[77]

C.-R. Lin, Ning-Han-Liu, Y.-H. Wu, A.L.P. Chen, Music classification using significant repeating patterns, in: Lecture notes on computer science, Vol. 2973, Springer, Berlin, Heidelberg, 2004, pp. 506-518.

[78]

Mackay, C. (2013). jMIR 2.0. http://jmir.sourceforge.net/.

[79]

McKay, C. (2003). Using neural networks for musical genre classification. http://music.mcgill.caa/cmckay/projects.html.

[80]

McKay, C. (2004). Automatic classiffication of MIDI recordings. Master's thesis.

[81]

C. McKay, J.A. Burgoyne, J. Hockman, J.B.L. Smith, G. Vigliensoni, I. Fujinaga, Evaluating the genre classification performance of lyrical features relative to audio, symbolic and cultural features, 2010.

[82]

C. McKay, R. Fiebrink, D. McEnnis, B. Li, I. Fujinaga, Ace: A framework for optimizing music classification, Queen Mary University of London, London, UK, 2005.

[83]

C. McKay, I. Fujinaga, Automatic genre classification using large high-level musical feature sets, 2004.

[84]

C. McKay, I. Fujinaga, Automatic music classification and the importance of instrument identification, 2005.

[85]

C. McKay, I. Fujinaga, The Bodhidharma system and the results of the MIREX 2005 symbolic genre classification contest, 2005.

[86]

C. McKay, I. Fujinaga, jsymbolic: A feature extrator for MIDI files, 2006.

[87]

C. McKay, I. Fujinaga, Musical genre classification: Is it worth pursuing and how can it be improved?, 2006.

[88]

C. McKay, I. Fujinaga, Combining features extracted from audio, symbolic and cultural sources, 2008.

[89]

C. McKay, I. Fujinaga, Improving automatic music classification performance by extracting features from different types of data, 2010.

[90]

M.M. Mostafa, N. Billor, Recognition of western style musical genres using machine learning techniques, Expert Systems with Applications, 36 (2009) 11378-11389.

Digital Library

[91]

D. Müllensiefen, Fantastic: Feature ANalysis Technology Accessing STatistics (In a Corpus): Technical report v1.5, Goldsmiths University of London, 2009.

[92]

D. Müllensiefen, K. Frieler, Optimizing measures of melodic similarity for the exploration of a large folk song database, 2004.

[93]

A.C. North, D.J. Hargreaves, Liking for musical styles, Musicae Scientiae, 1 (1997) 109-128.

[94]

T. Pérez-García, J.M. Iñesta, D. Rizo, metamidi: A tool for automatic metadata extraction from MIDI files, 2009.

[95]

Pérez-García, T., Iñesta, J. M., & Rizo, D. (2012). Resources. http://grfia.dlsi.ua.es/gen.php?id=resources.

[96]

T. Pérez-García, C. Pérez-Sancho, J.M. Iñesta, Harmonic and instrumental information fusion for musical genre classification, 2010.

[97]

C. Pérez-Sancho, P.J.P. de León, J.M. Iñesta, A comparison of statistical approaches to symbolic genre recognition, 2006.

[98]

C. Pérez-Sancho, J.M. Iñesta, J. Calera-Rubio, Style recognition through statistical event models, Journal of New Music Research, 34 (2005) 331-339.

[99]

C. Pérez-Sancho, D. Rizo, J.M. Iñesta, Stochastic text models for music categorization, in: Lecture notes on computer science, Vol. 5342, Springer, Berlin, Heidelberg, 2008, pp. 55-64.

Digital Library

[100]

C. Pérez-Sancho, D. Rizo, J.M. Iñesta, Genre classification using chords and stochastic language models, Connection Science, 21 (2009) 145-159.

Digital Library

[101]

C. Pérez-Sancho, D. Rizo, S. Kersten, R. Ramirez, Genre classification of music by tonal harmony, 2008.

[102]

A. Ruppin, H. Yeshurun, Midi music genre classification by invariant features, 2006.

[103]

M. Ryynänen, A. Klapuri, Automatic bass line transcription from streaming polyphonic audio, 2007.

[104]

N. Scaringella, G. Zoia, D. Mlynek, Automatic genre classification of music content - a survey, IEEE Signal Processing Magazine, 23 (2006) 133-141.

[105]

E. Selfridge-Field, Conceptual and representational issues in melodic comparison, in: Melodic similarity, concepts, procedures, and applications, Vol. 11, MIT Press, 1998, pp. 3-64.

[106]

M.-K. Shan, F.-F. Kuo, Music style mining and classification by melody, IEICE Transactions on Information and Systems, 3 (2003) 1-6.

[107]

M.-K. Shan, F.-F. Kuo, M.-F. Chen, Music style mining and classification by melody, 2002.

[108]

X. Shao, C. Xu, M.S. Kankanhalli, Unsupervised classification of music genre using hidden markov model, 2004.

[109]

C.N. Silla, A.A. Freitas, Novel top-down approaches for hierarchical classification and their application to automatic music genre classification, 2009.

[110]

U. Şimşekli, Automatic music genre classification using bass lines, 2010.

[111]

B. Snyder, Memory for music, in: The Oxford handbook of music psychology, Vol. 1, Oxford University Press, 2009, pp. 107-117.

[112]

B.L. Strum, Classification accuracy is not enough: On the evaluation of music genre recognition systems, Journal of Intelligent Information Systems, 41 (2013) 371-406.

Digital Library

[113]

G. Tzanetakis, P. Cook, Musical genre classification of audio signals, IEEE Transactions on Speech and Audio Processing, 10 (2002) 293-302.

[114]

G. Tzanetakis, A. Ermolinskyi, P. Cook, Pitch histograms in audio and symbolic music information retrieval, 2002.

[115]

G. Tzanetakis, A. Ermolinskyi, P. Cook, Pitch histograms in audio and symbolic music information retrieval, Journal of New Music Research, 32 (2003) 143-152.

[116]

A.L. Uitdenbogerd, J. Zobel, Manipulation of music for melody matching, ACM, New York, NY, USA, 1998.

[117]

J. Valverde-Rebaza, A. Soriano, L. Berton, F. de Oliveira, M. Cristina, A. De Andrade Lopes, Music genre classification using traditional and relational approaches, IEEE, 2014.

[118]

P. van Kranenburg, A. Volk, F. Wiering, A comparison between global and local features for computational classification of folk song melodies, Journal of New Music Research, 42 (2013) 1-18.

[119]

V.N. Vapnik, Springer-Verlag, New York, USA, 1995.

[120]

V.N. Vapnik, A.Y. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities, Theory of Probability and Its Applications, 16 (1971) 283-305.

[121]

G. Velarde, T. Weyde, D. Meredith, An approach to melodic segmentation and classification based on filtering with the haar wavelet, Journal of New Music Research, 42 (2013) 325-345.

[122]

G. Velarde, T. Weyde, D. Meredith, Wavelet-filtering of symbolic music representations for folk tune segmentation and classification, Meertens Institute, Department of Information and Computing Sciences, Utrecht University, Amsterdam, Netherlands, 2013.

[123]

T. Völkel, J. Abeßer, C. Dittmar, H. Broßmann, Automatic genre classification of Latin American music using characteristic rhythmic patterns, 2010.

[124]

L. Wang, M. Sugiyama, C. Yang, K. Hatano, J. Feng, Theory and algorithm for learning with dissimilarity functions, Neural Computation, 21 (2009) 1459-1484.

Digital Library

Cited By

Cai XZhang H(2024)Fisher Discriminative Embedding Low-Rank Sparse Representation for Music Genre ClassificationCircuits, Systems, and Signal Processing10.1007/s00034-024-02696-043:8(5139-5168)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00034-024-02696-0
Poltronieri A(2023)Knowledge-Based Multimodal Music SimilarityThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_41(224-233)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-43458-7_41
Lisena PMeroño-Peñuela ATroncy RAlam MBuscaldi DCochez MOsborne FRefogiato Recupero DSack H(2022)MIDI2vecSemantic Web10.3233/SW-21044613:3(357-377)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/SW-210446
Show More Cited By

A survey on symbolic data-based music genre classification
1. Applied computing
  1. Arts and humanities
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

Recommendations

A Survey of Audio-Based Music Classification and Annotation

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. ...
Music Genre Classification and Feature Comparison using ML
ICMLT '22: Proceedings of the 2022 7th International Conference on Machine Learning Technologies

An essential feature of the music is the genre, which can be considered a high-level description of an individual piece of music. In this sense, genre as a music feature is similar to typical descriptive features from the ML perspective. Although a ...
Improving Automatic Music Genre Classification Systems by Using Descriptive Statistical Features of Audio Signals
Artificial Intelligence in Music, Sound, Art and Design
Abstract
Automatic music genre classification systems are vital nowadays because the traditional music genre classification process is mostly implemented without following a universal taxonomy and the traditional process for audio indexing is prone to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 60, Issue C

October 2016

321 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 30 October 2016

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Cai XZhang H(2024)Fisher Discriminative Embedding Low-Rank Sparse Representation for Music Genre ClassificationCircuits, Systems, and Signal Processing10.1007/s00034-024-02696-043:8(5139-5168)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00034-024-02696-0
Poltronieri A(2023)Knowledge-Based Multimodal Music SimilarityThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_41(224-233)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-43458-7_41
Lisena PMeroño-Peñuela ATroncy RAlam MBuscaldi DCochez MOsborne FRefogiato Recupero DSack H(2022)MIDI2vecSemantic Web10.3233/SW-21044613:3(357-377)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/SW-210446
Ning QShi J(2022)Artificial Neural Network for Folk Music Style ClassificationMobile Information Systems10.1155/2022/92034202022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9203420
Coca Salazar A(2022)Hierarchical mining with complex networks for music genre classificationDigital Signal Processing10.1016/j.dsp.2022.103559127:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.dsp.2022.103559
Li YZhang ZDing HChang L(2022)Music genre classification based on fusing audio and lyric informationMultimedia Tools and Applications10.1007/s11042-022-14252-682:13(20157-20176)Online publication date: 29-Dec-2022
https://dl.acm.org/doi/10.1007/s11042-022-14252-6
Cai XZhang H(2022)Music genre classification based on auditory image, spectral and acoustic featuresMultimedia Systems10.1007/s00530-021-00886-328:3(779-791)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00530-021-00886-3
Chaki J(2021)Pattern analysis based acoustic signal processing: a survey of the state-of-artInternational Journal of Speech Technology10.1007/s10772-020-09681-324:4(913-955)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s10772-020-09681-3
Ramírez JFlores M(2020)Machine learning for music genre: multifaceted review and experimentation with audiosetJournal of Intelligent Information Systems10.1007/s10844-019-00582-955:3(469-499)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10844-019-00582-9
Li JLuo JDing JZhao XYang X(2019)Regional classification of Chinese folk songs based on CRF modelMultimedia Tools and Applications10.1007/s11042-018-6637-678:9(11563-11584)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-6637-6
Show More Cited By

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents