Abstract
Synthesis of realistic bowed-string instrument sound is a difficult task due to the diversified playing techniques and the ever-changing dynamics which cause rapidly varying characteristics. The noise part closely related to the dynamic bow-string interaction is also regarded as an indispensable part of the musical sound. Neural networks have been applied to sound synthesis for years. In this paper, a source filter synthesis model combined with a Long-Short-Term-Memory (LSTM) RNN predictor and a self-organized granular wavetable is proposed. The synthesis sound can be close to the recorded tones of a target bowed-string instrument. The timbre and the noise are both well preserved. Changes of pitch and dynamics can be easily achieved in real time, too.
Original language | English |
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Publication status | Published - 2020 |
Event | 148th Audio Engineering Society International Convention 2020 - Vienna, Virtual, Online, Austria Duration: 2020 Jun 2 → 2020 Jun 5 |
Conference
Conference | 148th Audio Engineering Society International Convention 2020 |
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Country/Territory | Austria |
City | Vienna, Virtual, Online |
Period | 20-06-02 → 20-06-05 |
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Acoustics and Ultrasonics