The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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Voice Driven Sound Sketch for Animation Authoring Tools

애니메이션 저작도구를 위한 음성 기반 음향 스케치

  • 권순일 (세종대학교 컴퓨터공학부 디지털콘텐츠)
  • Received : 2009.12.10
  • Accepted : 2010.02.11
  • Published : 2010.04.28
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Abstract

Authoring tools for sketching the motion of characters to be animated have been studied. However the natural interface for sound editing has not been sufficiently studied. In this paper, I present a novel method that sound sample is selected by speaking sound-imitation words(onomatopoeia). Experiment with the method based on statistical models, which is generally used for pattern recognition, showed up to 97% in the accuracy of recognition. In addition, to address the difficulty of data collection for newly enrolled sound samples, the GLR Test based on only one sample of each sound-imitation word showed almost the same accuracy as the previous method.

애니메이션 캐릭터의 동작을 만들어내기 위해 펜으로 스케치하는 형식의 인터페이스를 이용하는 저작 도구들이 연구되어 왔지만, 아직까지 음향적인 요소에 있어서 직관적인 인터페이스를 사용하여 만들어내는 방법은 연구되지 않았다. 본 논문에서는 사용자가 음향과 대응되는 의성어의 발성을 통하여 표현하면 이에 대응되는 음향샘플이 선택되어 삽입되는 방법을 제안하고자 한다. 일반적으로 사용되는 통계적 모델을 기반으로 하는 패턴인식 방법을 이용하여 의성어 발성만으로 대응되는 음향샘플을 어느 정도 인식할 수 있는지를 실험해본 결과 의성어의 음성샘플을 이용한 경우 최대 97%의 인식률을 얻을 수 있었다. 또한 새로운 음향샘플 등록 시에 발생하는 음성데이터 수집의 어려움을 극복하기 위하여 음성모델을 만드는 대신에 의성어의 음성샘플 하나만 사용하는 GLR Test를 활용해보니 기존의 방법과 거의 대등한 인식률을 실험적으로 확인할 수 있었다.

Keywords

References

  1. M. Thorne, D. Burke, and M. Panne, "Motion Doodles: An Interface for Sketching Character Motion," ACM Transactions on Graphics, Vol. 23, pp.424-431, 2004. https://doi.org/10.1145/1015706.1015740
  2. T. Nakano, M. Goto, J. Ogata, and Y. Hiraga, "Voice Drummer: A Music Notation Interface of Drum Sounds Using Voice Percussion Input," Proc. of ACM Symposium on User Interface Software and Technology (UIST), pp.49-50, 2005.
  3. Z. Wang and M. Panne, "Walk to here: A Voice Driven Animation System," Proc. of Eurographics/ ACM SIGGRAPH Symposium on Computer Animation, pp.16-20, 2006.
  4. O. Gillet and G. Richard, "Indexing and Querying Drum Loops Databases," Proc. of International workshop on Content Based on Multimedia and Indexing (CBMI'05), Riga, Latvia, 2005(6).
  5. K. Ishihara, Y. Tsubota, and H. G. Okuno, "Automatic Transformation of Environmental Sounds into Sound-Imitation Words Baed on Japanese Syllable Structure," Proc. of European Conference on Speech Communication and Technology, pp.3185-3188, 2003.
  6. K. Ishihara, T. Nakatani, T. Ogata, and H. G. Okuno, "Automatic Sound-Imitation Word Recognition from Environmental Sounds Focusing on Ambiguity Problem in Determining Phonemes," Lecture Note on Artificial Intelligence, Vol.3157, pp.909-918, 2004. https://doi.org/10.1007/978-3-540-28633-2_96
  7. T. C. Andringa and M. E. Niessen, "Real-world sound recognition: A recipe," Proc. of the 1st Workshop on Learning Semantics in Audio Signals(LSAS 2006), pp.106-118, 2006.
  8. R. Duda, D. Stork, and P. Hart, Pattern Classification, Wiley-Interscience Pub, 2/E, 2000.
  9. J. S. Baek, "A Generalized Likelihood Ratio Test in Outlier Detection," Korean Journal of Applied Statistics, Vol.4, pp.225-237, 1994.
  10. R. C. Davis, B. Colwell, and J. A. Landay, "K-sketch: a `kinetic' sketch pad for novice animators," Proc. of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pp.413-422, 2008. https://doi.org/10.1145/1357054.1357122
  11. M. Battermann, S. Heise, and J. Loviscach, "SonoSketch: Querying Sound Effect Databases through Painting," Proc. of 126th AES Convention, Paper Number 7794, 2009.

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