Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 13/2021

12.11.2020 | Original Article

Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering

verfasst von: Raúl V. Casaña-Eslava, Ian H. Jarman, Sandra Ortega-Martorell, Paulo J. G. Lisboa, José D. Martín-Guerrero

Erschienen in: Neural Computing and Applications | Ausgabe 13/2021

Einloggen

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

search-config
loading …

Abstract

Probabilistic classifiers induce a similarity metric at each location in the space of the data. This is measured by the Fisher Information Matrix. Pairwise distances in this Riemannian space, calculated along geodesic paths, can be used to generate a similarity map of the data. The novelty in the paper is twofold; to improve the methodology for visualisation of data structures in low-dimensional manifolds, and to illustrate the value of inferring the structure from a probabilistic classifier by metric learning, through application to music data. This leads to the discovery of new structures and song similarities beyond the original genre classification labels. These similarities are not directly observable by measuring Euclidean distances between features of the original space, but require the correct metric to reflect similarity based on genre. The results quantify the extent to which music from bands typically associated with one particular genre can, in fact, crossover strongly to another genre.
Vorheriger Artikel Hesitant fuzzy N-soft ELECTRE-II model: a new framework for decision-making
Nächster Artikel Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm

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

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

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+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
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
Literatur
1.
Amari SI (1998) Natural gradient works efficiently in learning. Neural comput 10(2):251–276CrossRef
2.
Amari Si WuS (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12(6):783–789CrossRef
3.
Bogdanov D, Serra J, Wack N, Herrera P (2009) From low-level to high-level: Comparative study of music similarity measures. In: 2009 11th IEEE international symposium on multimedia, pp 453–458. IEEE
4.
5.
6.
Casaña-Eslava RV, Jarman IH, Lisboa PJ, Martín-Guerrero JD (2017) Quantum clustering in non-spherical data distributions: finding a suitable number of clusters. Neurocomputing 268:127–141CrossRef
7.
Casaña-Eslava RV, Martín-Guerrero JD, Ortega-Martorell S, Lisboa PJ, Jarman IH (2019) Scalable implementation of measuring distances in a riemannian manifold based on the fisher information metric. In: 2019 International joint conference on neural networks (IJCNN), pp 1–7. IEEE
8.
Casey MA, Veltkamp R, Goto M, Leman M, Rhodes C, Slaney M (2008) Content-based music information retrieval: current directions and future challenges. Proc IEEE 96(4):668–696CrossRef
9.
Chambers SJ, Jarman IH, Etchells TA, Lisboa PJG (2013) Inference of number of prototypes with a framework approach to k-means clustering. Int J Biomed Eng Technol 13(4):323–340CrossRef
10.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learning 20(3):273–297MATH
11.
Cox MA, Cox TF (2008) Multidimensional scaling. In: Chen C, Härdle WK, Unwin A (eds) Handbook of data visualization, Springer, Heidelberg, pp 315–347CrossRef
12.
13.
14.
Goto M, Goto T (2005) Musicream: New music playback interface for streaming, sticking, sorting, and recalling musical pieces. In: ISMIR, pp 404–411
15.
Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3–4):325–338MathSciNetCrossRef
16.
Hamasaki M, Goto M (2013) Songrium: A music browsing assistance service based on visualization of massive open collaboration within music content creation community. In: Proceedings of the 9th international symposium on open collaboration, pp 1–10
17.
Haykin SS (2009) Neural networks and learning machines, 3rd edn. Pearson Education, Upper Saddle River
18.
Horn D, Gottlieb A (2001) Algorithm for data clustering in pattern recognition problems based on quantum mechanics. Phys Rev Lett 88(1):018702CrossRef
19.
Horn D, Gottlieb A (2001) The method of quantum clustering. Proc Neural Inf Process Syst NIPS 2001:769–776
20.
Jaakkola T, Haussler D et al (1999) Exploiting generative models in discriminative classifiers. In: Kearns MJ, Solla SA, Cohn DA (eds) Advances in neural information processing systems 11. MIT Press, pp 487–493
21.
Jones MC, Downie JS, Ehmann AF (2007) Human similarity judgments: Implications for the design of formal evaluations. In: ISMIR, pp 539–542
22.
Kaski S, Sinkkonen J (2000) Metrics that learn relevance. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks. IJCNN 2000. Neural computing: new challenges and perspectives for the new millennium, vol 5, pp 547–552 https://​doi.​org/​10.​1109/​IJCNN.​2000.​861526
23.
24.
Kim JH, Tomasik B, Turnbull D (2009) Using artist similarity to propagate semantic information. ISMIR 9:375–380
25.
Knees P, Pampalk E, Widmer G (2004) Artist classification with web-based data. In: ISMIR
26.
Knees P, Schedl M, Pohle T, Widmer G (2006) An innovative three-dimensional user interface for exploring music collections enriched. In: Proceedings of the 14th ACM international conference on Multimedia, pp 17–24
27.
Kullback S (1997) Information theory and statistics. Courier Corporation, New YorkMATH
28.
29.
Li Y, Wang Y, Wang Y, Jiao L, Liu Y (2016) Quantum clustering using kernel entropy component analysis. Neurocomputing 202:36–48CrossRef
30.
Lippens S, Martens JP, De Mulder T (2004) A comparison of human and automatic musical genre classification. In: 2004 IEEE international conference on acoustics, speech, and signal processing, vol 4, pp. iv–iv. IEEE
31.
Lisboa PJG, Etchells TA, Jarman IH, Chambers SJ (2013) Finding reproducible cluster partitions for the k-means algorithm. BMC Bioinf 14(Suppl. 1):S8CrossRef
32.
Mandel MI, Pascanu R, Eck D, Bengio Y, Aiello LM, Schifanella R, Menczer F (2011) Contextual tag inference. ACM Trans Multimed Comput Commun Appl (TOMM) 7(1):1–18
33.
McKay C (2010) Automatic music classification with jMIR. Citeseer
34.
McKay C, Fujinaga I, Depalle P (2005) jaudio: A feature extraction library. In: Proceedings of the international conference on music information retrieval, pp 600–603
35.
Miotto R, Barrington L, Lanckriet GR (2010) Improving auto-tagging by modeling semantic co-occurrences. In: ISMIR, pp 297–302
36.
37.
38.
Newman ME (2004) Detecting community structure in networks. Eur Phys J B Conden Matter Complex Syst 38(2):321–330CrossRef
39.
Parisi L, RaviChandran N, Manaog ML (2020) A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis. Neural Comput Appl 32(8):3839–3852CrossRef
40.
Platt J et al (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10(3):61–74
41.
Rao CR (1992) Information and the accuracy attainable in the estimation of statistical parameters. In: Kotz S, Johnson NL (eds) Breakthroughs in statistics. Springer, New York, pp 235–247CrossRef
42.
Ruiz H, Etchells TA, Jarman IH, Martín JD, Lisboa PJ (2013) A principled approach to network-based classification and data representation. Neurocomputing 112:79–91CrossRef
43.
Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 18(5):401–409CrossRef
44.
Schedl M, Flexer A, Urbano J (2013) The neglected user in music information retrieval research. J Intell Inf Syst 41(3):523–539CrossRef
45.
Schedl M, Gutiérrez EG, Urbano J (2014) Music information retrieval: Recent developments and applications. Foundations and Trends in Information Retrieval. 2014 Sept 12; 8 (2-3): 127–261
46.
Schedl M, Pohle T, Knees P, Widmer G (2011) Exploring the music similarity space on the web. ACM Trans Inf Syst (TOIS) 29(3):1–24CrossRef
47.
Schindler A, Mayer R, Rauber A (2012) Facilitating comprehensive benchmarking experiments on the million song dataset. In: ISMIR, pp 469–474
48.
Schindler A, Rauber A (2012) Capturing the temporal domain in echonest features for improved classification effectiveness. In: International workshop on adaptive multimedia retrieval, Springer, pp 214–227
49.
Seyerlehner K, Schedl M, Pohle T, Knees P (2010) Using block-level features for genre classification, tag classification and music similarity estimation. Submission to Audio Music Similarity and Retrieval Task of MIREX 2010
50.
Sordo M et al (2012) Semantic annotation of music collections: A computational approach. Ph.D. thesis, Universitat Pompeu Fabra
51.
52.
Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5):293–302CrossRef
53.
Urbano J (2013) Evaluation in audio music similarity. Ph.D. thesis, Universidad Carlos III de Madrid
54.
Urbano J, Morato J, Marrero M, Martín D (2010) Crowdsourcing preference judgments for evaluation of music similarity tasks. In: ACM SIGIR workshop on crowdsourcing for search evaluation, ACM New York, pp 9–16
55.
Vincent P, Bengio Y (2003) Manifold parzen windows. In: Advances in neural information processing systems, pp 849–856
56.
57.
Young G, Householder AS (1938) Discussion of a set of points in terms of their mutual distances. Psychometrika 3(1):19–22CrossRef
58.
Zelnik-Manor L, Perona P (2004) Self-tuning spectral clustering. Adv Neural Inf Process Syst 17:1601–1608
59.
Zhang YC, Séaghdha DÓ, Quercia D, Jambor T (2012) Auralist: introducing serendipity into music recommendation. In: Proceedings of the fifth ACM international conference on Web search and data mining, pp 13–22
Metadaten
Titel
Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering
verfasst von
Raúl V. Casaña-Eslava
Ian H. Jarman
Sandra Ortega-Martorell
Paulo J. G. Lisboa
José D. Martín-Guerrero
Publikationsdatum
12.11.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 13/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05499-x

Weitere Artikel der Ausgabe 13/2021

Neural Computing and Applications 13/2021 Zur Ausgabe

S.I. : DICTA 2019

Fixed-Lens camera setup and calibrated image registration for multifocus multiview 3D reconstruction

Original Article

Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification

Original Article

Analysis of acceptable additive consistency and consensus of group decision making with interval-valued hesitant fuzzy preference relations

Original Article

Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools

Original Article

Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm

Original Article

RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild

Premium Partner

    Bildnachweise