TY - GEN
T1 - Sparse modeling of musical instruments sounds in time-frequency domain
AU - Ogi, Hiromu
AU - Yatabe, Kohei
AU - Oikawa, Yasuhiro
AU - Miyagi, Yusuke
AU - Oishi, Koji
N1 - Publisher Copyright:
© 2019 Proceedings of the International Congress on Acoustics. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Sample-based synthesis is one of the most common synthesis methods of the digital synthesizer. It can synthesize musical instrument sounds well because it plays recorded sounds instead of generating sounds such as additive and frequency modulation syntheses. The recorded sounds are usually stored in the time domain. If the recorded sounds are stored in the time-frequency domain, sound processing can be simplified. Users can process audio signals intuitively by operating the time-frequency bins of the spectrograms. However, the representation of sounds in the time-frequency domain usually requires more memory than that in the time domain. In synthesizers, if much data is needed, hardware costs are increased correspondingly. Therefore, a technique to reduce the amount of data is required. In this paper, to reduce the amount of data, we introduce a sparse modeling technique for musical instrument sounds in the time-frequency domain. We propose an optimization algorithm of sparse modeling with four shrinkage operators. Numerical experiments show that the data quantity of the signals reduced by over 95 percent by using the technique.
AB - Sample-based synthesis is one of the most common synthesis methods of the digital synthesizer. It can synthesize musical instrument sounds well because it plays recorded sounds instead of generating sounds such as additive and frequency modulation syntheses. The recorded sounds are usually stored in the time domain. If the recorded sounds are stored in the time-frequency domain, sound processing can be simplified. Users can process audio signals intuitively by operating the time-frequency bins of the spectrograms. However, the representation of sounds in the time-frequency domain usually requires more memory than that in the time domain. In synthesizers, if much data is needed, hardware costs are increased correspondingly. Therefore, a technique to reduce the amount of data is required. In this paper, to reduce the amount of data, we introduce a sparse modeling technique for musical instrument sounds in the time-frequency domain. We propose an optimization algorithm of sparse modeling with four shrinkage operators. Numerical experiments show that the data quantity of the signals reduced by over 95 percent by using the technique.
KW - Alternating direction methods of multipliers (ADMM)
KW - Sample-based synthesis
KW - Sparse modeling
KW - Spectrogram
UR - http://www.scopus.com/inward/record.url?scp=85099329498&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099329498&partnerID=8YFLogxK
U2 - 10.18154/RWTH-CONV-239606
DO - 10.18154/RWTH-CONV-239606
M3 - Conference contribution
AN - SCOPUS:85099329498
T3 - Proceedings of the International Congress on Acoustics
SP - 6467
EP - 6474
BT - Proceedings of the 23rd International Congress on Acoustics
A2 - Ochmann, Martin
A2 - Michael, Vorlander
A2 - Fels, Janina
PB - International Commission for Acoustics (ICA)
T2 - 23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019
Y2 - 9 September 2019 through 23 September 2019
ER -