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Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues pp 376–386Cite as

Comparing the Rhythmical Characteristics of Speech and Music – Theoretical and Practical Issues

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6456))

Abstract

By comparing the features of music and speech in intelligent audio signal processing, both related research fields might benefit from each other. Music and speech serve as a way for humans to express themselves. The aim of this study is to show similarities and differences between music and speech by comparing the hierarchical structures with an emphasis on rhythm. Especially examining the temporal structure of music and speech could lead to new interesting features that improve existing technology. For example utilizing rhythm in synthetic speech is still an open issue as well as rhythmic features have to be improved for music in the fields of semantic search and music similarity retrieval. Theoretical aspects of rhythm in speech and music are discussed as well as practical issues in speech and music research. To show that common approaches are inherently feasible, an algorithm for onset detection is applied to speech and musical signals.

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References

  1. Spiegelbild der Sprache - Neurokognition von Musik (May 2010), http://www.cbs.mpg.de/institute/foci/mirror

  2. Bello, J.P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., Sandler, M.B.: A tutorial on onset detection in musical signals. IEEE Transactions on Speech and Audio Processing 13(5), 1035–1047 (2005)

    Article  Google Scholar 

  3. Caruntu, A., Toderean, G., Nica, A.: Automatic silence/unvoiced/voiced classifcation of speech using a modified teager energy feature. In: WSEAS Int. Conf. on Dynamical Systems and Control, Venice, Italy, pp. 62–65 (2005)

    Google Scholar 

  4. Collins, N.: Towards Autonomous Agents for Live Computer Music: Realtime Machine Listening and Interactive Music Systems. PhD thesis, Centre for Science and Music, Faculty of Music, University of Cambridge (2006)

    Google Scholar 

  5. Cummins, F.: Rhythmic Coordination in English Speech: An Experimental Study. PhD thesis, Indiana University (1997)

    Google Scholar 

  6. Cushing, I.R., Dellwo, V.: The role of speech rhythm in attending to one of two simultaneous speakers. In: Speech Prosody, 5th International Conference, Chicago, Illinois (2010)

    Google Scholar 

  7. Dauer, R.M.: Stress-timing and syllable-timing reanalyzed. Journal of Phonetics 11, 51–62 (1983)

    Google Scholar 

  8. Dixon, S., Pampalk, E., Widmer, G.: Classification of dance music by periodicity patterns. In: ISMIR, 4th International Society for Music Information Retrieval Conference, Baltimore, USA, pp. 159–165 (2003)

    Google Scholar 

  9. Gouyon, F., Dixon, S.: A review of automatic rhythm description systems. Computer Music Journal 29(1), 34–35 (2005)

    Article  Google Scholar 

  10. Gouyon, F., Dixon, S., Pampalk, E., Widmer, G.: Evaluating rhythmic descriptors for musical genre classification. In: AES, 25th International Conference, London, UK (June 2004)

    Google Scholar 

  11. Hübler, S., Wolff, M., Eichner, M.: Vergleich statistischer Klassifikatoren zur Ermittlung musikalischer Aspekte. In: Hoffmann, R. (ed.) Elektronische Sprachsignalverarbeitung. Tagungsband der 20. Konferenz, Dresden, 21. - 23. 9, of Studientexte zur Sprachkommunikation, Dresden, Germany, vol. 53, pp. 338–345 (September 2009)

    Google Scholar 

  12. Hirst, D.: The rhythm of text and the rhythm of utterances: from metrics to models. In: Interspeech, 10th Annual Conference of the International Speech Communication Association, Brighton, UK, pp. 1519–1522 (2009)

    Google Scholar 

  13. Hoffmann, R., Eichner, M., Wolff, M.: Analysis of verbal and nonverbal acoustic signals with the dresden UASR system. In: Esposito, A., Faundez-Zanuy, M., Keller, E., Marinaro, M. (eds.) COST Action 2102. LNCS (LNAI), vol. 4775, pp. 200–218. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Keller, E.: A phonetician’s view of signal generation for speech synthesis. In: Vich, R. (ed.) Electronic Speech Signal Processing, 16th Conference, Prague. Studientexte zur Sprachkommunikation, vol. 36, pp. 13–20. TUDpress, Dresden (2005)

    Google Scholar 

  15. Keller, E.: Beats for individual timing variation. In: Esposito, A., Bratanic, M., Keller, E., Marinaro, M. (eds.) The Fundamentals of Verbal and Non-verbal Communication and the Biometric Issue, pp. 115–128. IOS Press, Amsterdam (2007)

    Google Scholar 

  16. Keller, E.: From sound to rhythm expectancy (Tutorial). 5o Convegno Nazionale AISV. Università de Zurigo, Switzerland (2009)

    Google Scholar 

  17. Kühne, M., Wolff, M., Eichner, M., Hoffmann, R.: Voice activation using prosodic features. In: Interspeech, 8th International Conference on Spoken Language Processing, Jeju, Korea, pp. 3001–3004 (2004)

    Google Scholar 

  18. Klapuri, A.: Sound onset detection by appliying psychoacoustic knowledge. In: ICASSP, Phoenix, USA, vol. 6, pp. 3089–3092 (March1999)

    Google Scholar 

  19. Kompe, R.: Prosody in Speech Understanding Systems. LNCS, vol. 1307. Springer, Heidelberg (1997)

    Google Scholar 

  20. Kotnik, B., Sendorek, P., Astrov, S., Koc, T., Ciloglu, T., Fernández, L.D., Banga, E.R., Höge, H., Kacic, Z.: Evaluation of voice activity and voicing detection. In: Interspeech, 9th Annual Conference of the International Speech Communication Association, Brisbane, Australia, pp. 1642–1645 (2008)

    Google Scholar 

  21. Krishna, A.G., Sreenivas, T.V.: Music instrument recognition: From isolated notes to solo phrases. In: ICASSP, pp. 265–268 (2004)

    Google Scholar 

  22. Lehiste, I.: Isochrony reconsidered. Journal of Phonetics 5, 253–263 (1977)

    Google Scholar 

  23. Lehrdal, F., Jackendoff, R.: The Generative Theory of Tonal Music. MIT Press, Cambridge (1983)

    Google Scholar 

  24. Leuschel, A., Docherty, G.J.: Prosodic assessment of dysarthria. In: Disorders of Motor Speech: Assessment, Treatment and Clinical Characterization, pp. 155–178. Paul H Brookes Publishing Co. Inc. (1996)

    Google Scholar 

  25. London, J.: Grove music online: Rhythm (April 2010), http://www.grovemusic.com

  26. Aniruddh Patel, D.: Language, music, syntax and the brain. Nature Neuroscience 6(7), 674–681 (2003)

    Article  Google Scholar 

  27. Volín, J., Pollák, P.: The dynamic dimension of the global speech-rhythm attributes. In: Interspeech, 10th Annual Conference of the International Speech Communication Association, Brighton, UK, pp. 1543–1546 (2009)

    Google Scholar 

  28. Werner, S., Eichner, M., Wolff, M., Hoffmann, R.: Toward spontanuos speech synthesis - utilizing language model information in tts. IEEE Transactions on Speech and Audio Processing 12(4), 436–445 (2004)

    Article  Google Scholar 

  29. Wolkowicz, J., Kesel, V.: Predicting development of research in music based on parallel with natural language processing. In: ISMIR, 11th International Society for Music Information Retrieval Conference, Utrecht, Netherlands, pp. 665–667 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory of Acoustics and Speech Communication, Technische Universität Dresden, 01062, Dresden, Germany

    Stephan Hübler & Rüdiger Hoffmann

Authors
  1. Stephan Hübler
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  2. Rüdiger Hoffmann
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Editor information

Editors and Affiliations

  1. Institute for Advanced Scientific Studies, Second University of Naples, and IIASS, Via Pellegrino 19, 84019, Vietri sul Mare (SA), Italy

    Anna Esposito

  2. Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Via Diocleziano 328, 80124, Napoli, Italy

    Antonietta M. Esposito

  3. Dipartemento di Ingegneria dell’ Informazione, Seconda Università di Napoli, Via Roma 29, 81031, Aversa (CE), Italy

    Raffaele Martone

  4. Department of Humanities and Social Sciences, Anatolia College/ACT, Kennedy Street, 55510, Pylaia, Greece

    Vincent C. Müller

  5. Departmnet of Physics "E.R. Caoamoeööp", University of Salerno and IIASS, International Institute for Advanced Scientific Studies, 84081, Baronissi (SA), Italy

    Gaetano Scarpetta

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Hübler, S., Hoffmann, R. (2011). Comparing the Rhythmical Characteristics of Speech and Music – Theoretical and Practical Issues. In: Esposito, A., Esposito, A.M., Martone, R., Müller, V.C., Scarpetta, G. (eds) Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues. Lecture Notes in Computer Science, vol 6456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18184-9_33

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  • DOI: https://doi.org/10.1007/978-3-642-18184-9_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18183-2

  • Online ISBN: 978-3-642-18184-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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