nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples

verfasst von : Igor Vatolkin

Erschienen in: Computational Intelligence in Music, Sound, Art and Design

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Instrument recognition in polyphonic audio recordings is a very complex task. Most research studies until now were focussed on the recognition of Western instruments in Western classical and popular music, but also an increasing number of recent works addressed the classification of ethnic/world recordings. However, such studies are typically restricted to one kind of music and do not measure the bias of “Western” effect, i.e., the danger of overfitting towards Western music when the classification models are optimised only for such tracks. In this paper, we analyse the performance of several instrument classification models which are trained and optimised on polyphonic mixtures of Western instruments, but independently validated on mixtures created with randomly added ethnic samples. The conducted experiments include evolutionary multi-objective feature selection from a large set of audio signal descriptors and the estimation of individual feature relevance.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Fashion Design Aid System with Application of Interactive Genetic Algorithms

Nächstes Kapitel Exploring the Exactitudes Portrait Series with Restricted Boltzmann Machines

http://compmusic.upf.edu/publications, accessed on 15.11.2016.

http://theremin.music.uiowa.edu/MIS.html, accessed on 15.11.2016.

http://www.bestservice.de/en/ethno_world_5_professional__voices.html, accessed on 15.11.2016.

In this study, the reference point is (1,1): a theoretical solution which uses all features and leads to the classification error \(e=1\).

For all applied tests in this paper, we use a standard value of 5% for the significance level.

The statistical observations are shortened for simplicity reasons and should be interpreted with certain restrictions. Obviously, they hold only for tested instruments, mixtures, features, feature processing, and feature selection method.

Abdoli, S.: Iranian traditional music dastgah classification. In: Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR), pp. 275–280 (2011)

Agarwal, P., Karnick, H., Raj, B.: A comparative study of Indian and western music forms. In: Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), pp. 29–34 (2013)

Benetos, E., Holzapfel, A.: Automatic transcription of Turkish makam music. In: Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), pp. 355–360 (2013)

Brown, J.C., Houix, O., Mcadams, S.: Feature dependence in the automatic identification of musical woodwind instruments. J. Acoust. Soc. Am. 109(3), 1064–1072 (2001)CrossRef

Ding, C.H.Q., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3(2), 185–205 (2005)CrossRef

Eerola, T.: Are the emotions expressed in music genre-specific? An audio-based evaluation of datasets spanning classical, film, pop and mixed genres. J. New Music Res. 40(3), 349–366 (2011)CrossRef

Eerola, T., Ferrer, R.: Instrument library (MUMS) revised. Music Percept. 25(3), 253–255 (2008)CrossRef

Eggink, J., Brown, G.J.: Instrument recognition in accompanied sonatas and concertos. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 217–220 (2004)

Emmerich, M., Beume, N., Naujoks, B.: An EMO algorithm using the hypervolume measure as selection criterion. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 62–76. Springer, Heidelberg (2005). doi:10.1007/978-3-540-31880-4_5 CrossRef

10.

Eronen, A.J., Klapuri, A.: Musical instrument recognition using cepstral coefficients and temporal features. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 753–756 (2000)

11.

Essid, S., Richard, G., David, B.: Instrument recognition in polyphonic music based on automatic taxonomies. IEEE Trans. Audio Speech Lang. Process. 14(1), 68–80 (2006)CrossRef

12.

Fuhrmann, F.: Automatic musical instrument recognition from polyphonic music audio signals. Ph.D. thesis, Universitat Pompeu Fabra (2012)

13.

Gaikwad, S., Chitre, A.V., Dandawate, Y.H.: Classification of Indian classical instruments using spectral and principal component analysis based cepstrum features. In: Proceedings of the 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), pp. 276–279 (2014)

14.

Goto, M., Hashiguchi, H., Nishimura, T., Oka, R.: RWC music database: Music genre database and musical instrument sound database. In: Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR), pp. 229–230 (2003)

15.

Gunasekaran, S., Revathy, K.: Fractal dimension analysis of audio signals for Indian musical instrument recognition. In: Proceedings of the International Conference on Audio, Language and Image Processing (ICALIP), pp. 257–261 (2008)

16.

Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A.: Feature Extraction: Foundations and Applications. Springer, Heidelberg (2006)CrossRefMATH

17.

Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009)CrossRefMATH

18.

Heittola, T., Klapuri, A., Virtanen, T.: Musical instrument recognition in polyphonic audio using source-filter model for sound separation. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), pp. 327–332 (2009)

19.

Koduri, G.K., Miron, M., Serrà, J., Serra, X.: Computational approaches for the understanding of melody in carnatic music. In: Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR), pp. 263–268 (2011)

20.

Lartillot, O., Toiviainen, P.: MIR in Matlab (II): A toolbox for musical feature extraction from audio. In: Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR), pp. 127–130 (2007)

21.

Lashari, S.A., Ibrahim, R., Senan, N.: Soft set theory for automatic classification of traditional pakistani musical instruments sounds. In: Proceedings of the International Conference on Computer Information Science (ICCIS), pp. 94–99 (2012)

22.

Lidy, T., Silla Jr., C.N., Cornelis, O., Gouyon, F., Rauber, A., Kaestner, C.A.A., Koerich, A.L.: On the suitability of state-of-the-art music information retrieval methods for analyzing, categorizing and accessing non-Western and ethnic music collections. Signal Process. 90(4), 1032–1048 (2010)CrossRefMATH

23.

Livshin, A., Rodet, X.: The significance of the non-harmonic “noise” versis the harmonic series for musical instrument recognition. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR), pp. 95–100 (2006)

24.

McEnnis, D., McKay, C., Fujinaga, I.: jAudio: Additions and improvements. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR), pp. 385–386 (2006)

25.

Mierswa, I., Morik, K.: Automatic feature extraction for classifying audio data. Mach. Learn. J. 58(2–3), 127–149 (2005)CrossRefMATH

26.

Müller, M.: Information Retrieval for Music and Motion. Springer, Heidelberg (2007)CrossRef

27.

Müller, M., Ewert, S.: Chroma toolbox: MATLAB implementations for extracting variants of chroma-based audio features. In: Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR), pp. 215–220 (2011)

28.

Newton, M., Smith, L.: A neurally inspired musical instrument classification system based upon the sound onset. J. Acoust. Soc. Am. 131(6), 4785–4798 (2012)CrossRef

29.

Sandrock, T.: Multi-label feature selection with application to musical instrument recognition. Ph.D. thesis, Stellenbosch University (2013)

30.

Srinivasamurthy, A., Holzapfel, A., Serra, X.: In search of automatic rhythm analysis methods for Turkish and Indian art music. J. New Music Res. 43, 94–114 (2014)CrossRef

31.

Sturm, B.: Evaluating music emotion recognition: Lessons from music genre recognition? In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2013)

32.

Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10(5), 293–302 (2002)CrossRef

33.

Vatolkin, I., Preuß, M., Rudolph, G., Eichhoff, M., Weihs, C.: Multi-objective evolutionary feature selection for instrument recognition in polyphonic audio mixtures. Soft Comput. Fusion Found. Methodologies Appl. 16(12), 2027–2047 (2012)

34.

Vatolkin, I., Rudolph, G., Weihs, C.: Evaluation of album effect for feature selection in music genre recognition. In: Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), pp. 169–175 (2015)

35.

Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN), pp. 292–304 (1998)

Titel: Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples
verfasst von: Igor Vatolkin
Verlag: Springer International Publishing
Buch: Computational Intelligence in Music, Sound, Art and Design
Print ISBN: 978-3-319-55749-6

Electronic ISBN: 978-3-319-55750-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-55750-2_21

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner