An Automatic Synthesis of Musical Phrases from Multi-Pitch Samples

• Autorzy: Pluta M. , Spalek L. J. , Delekta R. J.

Identyfikatory

DOI

10.1515/aoa-2017-0026

• Języki publikacji: EN

Abstrakty

EN

Sound synthesizers are a natural element of a musician’s toolset. In music arrangement, samplers can often produce satisfactory results, but it requires a combination of manual and automatic methods that may be arduous at times. Concatenative Sound Synthesis reproduces many of natural performance- and expression-related nuances but at a cost of high demand for processing power. Here, another method of musical phrase synthesis aimed at music arrangement is presented that addresses these and other related issues. Contrary to common practice, we propose to record and utilize sound samples containing not one, but short sequences of pitches. In effect, natural pitch transitions are preserved and phrases appear to be much smoother, despite using very limited set of performance rules. The proof-of-concept implementation of the proposed method is discussed in detail along with attempts at optimizing the final sound effects, based on auditory tests. The limitations and future applications of the synthesizer are also discussed.

Słowa kluczowe

EN

sound synthesis; synthesis methods; sampling; Concatenative Sound Synthesis

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2017
• Tom: Vol. 42, No. 2
• Strony: 235--247
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr.

Twórcy

autor

Pluta M.

• Academy of Music in Krakow, Św. Tomasza 43, 31-027 Kraków, Poland

autor

Spalek L. J.

• Institute of Physics, Cracow Institute of Technology, Podchorążych 1, 30-084 Kraków, Poland

autor

Delekta R. J.

• Academy of Music in Krakow, Św. Tomasza 43, 31-027 Kraków, Poland

Bibliografia

• 1. Bresin R. (1998), Artificial neural networks based models for automatic performance of musical scores, Journal of New Music Research, 27, 239–270.
• 2. Bresin R., Friberg A., Sundberg J. (2002), Director Musices: The KTH Performance Rules System, Proceedings of SIGMUS-46, pp. 43–48, Kyoto.
• 3. Czerwiński A., Grzybek D., Krauze P., Łuczko J., Michalczyk K., Orkisz P., Pluta M., Radziszewski L., Saga M., Snamina J. (2015), GPU-based sound synthesis controlled with MIDI protocol [in Polish: Synteza dźwięku z wykorzystaniem procesora graficznego, sterowana przy użyciu protokołu MIDI], [in:] Selected issues of noise and vibration reduction systems [in Polish: Wybrane zagadnienia układów redukcji drgań i hałasu], Orkisz, P. [Ed.], pp. 40–52, Akademia Górniczo-Hutnicza, Kraków.
• 4. Delekta R. J., Pluta M. (2015), An implementation of the performance rules in the modified sampling synthesis of wind instruments [in Polish: Implementacja reguł wykonawczych w syntezie dźwięku instrumentów dętych zmodyfikowaną metodą samplingową], Proceedings of XVI International Symposium on Sound Engineering and Tonmeistering ISSET’2015, pp. 119–125, Warszawa.
• 5. Delekta R. J., Spalek L. J., Pluta M. (2016), The impact of selected parameters of a modified sampling synthesis on the result of its auditory assessment, Journal of Applied Mathematics and Physics, 4, 2, 221–226.
• 6. Friberg A., Bresin R., Sundberg J. (2006), Overview of the KTH rule system for music performance, Advances in Cognitive Psychology, 2, 2–3, 145–161.
• 7. Gabrielsson A. (1985), Interplay between analysis and synthesis in studies of music performance and music experience, Psychology of Music, 3, 59–86.
• 8. Klatt D. H. (1983), Review of Text-to-Speech Conversion for English, Journal of the Acoustics Society of America, 82 3, 737–793.
• 9. Lindemann E. (2007), Music Synthesis with Reconstructive Phrase Modeling. IEEE Signal Processing Magazine, 24, 2, 80–91.
• 10. London J. (2004), Hearing in Time: Psychological Aspects of Musical Meter, Oxford University Press, Oxford.
• 11. Maestre E., Ramırez R., Kersten S., Serra X. (2009), Expressive concatenative synthesis by reusing samples from real performance recordings, Computer Music Journal, 33, 4, 23–42.
• 12. Pluta M., Delekta, R. J. (2015), Technique to Seamlessly Connect Sound Samples in Sampling Synthesis, [in:] Progress of Acoustics 2015, Opieliński K. J. [Ed.], pp. 271–282, Polskie Towarzystwo Akustyczne, Wrocław.
• 13. Pluta M., Spalek L. J., Delekta R. J. (2016), A modified sampling synthesis for a realistic simulation of wind instruments – the design and implementation, Journal of Applied Mathematics and Physics, 4, 2, 215–220.
• 14. Prudon R. (2003), A Selection/Concatenation TTS Synthesis System, PhD Thesis, LIMSI, University of Paris XI.
• 15. Schwarz D., Beller G., Verbrugghe B., Britton S. (2006), Real-Time Corpus-Based Concatenative Synthesis with CataRT, Proceedings of the COST-G6 Conference on Digital Audio Effects (DAFx), pp. 279–282, Montreal.
• 16. Schwarz D., Cahen R., Britton S. (2008), Principles and applications of interactive corpus-based concatenative synthesis, Proceedings of the Journées d’Informatique Musicale (JIM).
• 17. Simon I., Basu S., Salesin D., Agrawala M. (2005), Audio Analogies: Creating New Music from an Existing Performance by Concatenative Synthesis, Proceedings of the 2005 International Computer Music Conference, pp. 65–72, San Francisco.
• 18. Smith III J. O. (1991), Viewpoints on the History of Digital Synthesis, Proceedings of the International Computer Music Conference ICMC-91, pp. 1–10, Montreal.
• 19. Widmer G. (1995), Modeling rational basis for musical expression Computer Music Journal, 19, 76–96.
• 20. Widmer G. (2002), Machine discoveries: A few simple, robust local expression principles, Journal of New Music Research, 31, 37–50.
• 21. Widmer G., Tobudic A. (2003), Playing Mozart by analogy: Learning multi-level timing and dynamics strategies, Journal of New Music Research, 32, 259–268.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).