## Abstracts
### S5. Oral Session 5: Sound Synthesis and Analysis  
Session Chair: [PERSON]  
S5.1. Experimental Verification of Dispersive Wave Propagation on Guitar Strings  
[PERSON], [PERSON], and [PERSON]  
Experimental research into the fundamental acoustic aspects of musical instruments and other sound generating devices is an important part of the history of musical acoustics and of physics in general. This paper presented experimental proof of dispersive wave propagation on metal guitar strings. The high resolution experimental data of string displacement are gathered using video-kymographic high-speed imaging of the vibrating string. The experimental data are indirectly compared against a dispersive Euler-Bernoulli type model described by a PDE. In order to detect the minor wave features associated with the dispersion and distinguish them from other effects present, such as frequency-dependent dissipation, a second model lacking the dispersive (stiffness) term is used. Unsurprisingly, the dispersive effects are shown to be minor but definitively present. The results and methods presented here in general should find application in string instrument acoustics. S5.2. Real-Time Modeling of Audio Distortion Circuits with Deep Learning  
[PERSON], [PERSON], and [PERSON]  
This paper studies deep neural networks for the modeling of audio distortion circuits. The selected approach is black-box modeling, which estimates model parameters based on the measured input and output signals of the device. Three common audio distortion pedals having a different circuit configuration and their own distinctive sonic character have been chosen for this study: The Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Mu Pi. A feedforward deep neural network, which is a variant of the WaveNet architecture, is proposed for modeling these devices. The size of the receptive field of the neural network is selected based on the measured impulse-response length of the circuits. A real-time implementation of the deep neural network is presented, and it is shown that the trained models can be run in real-time on a modern desktop computer. Furthermore, it is shown that three minutes of audio is a sufficient amount of data for training the models. The deep neural network studied in this work is useful for real-time virtual analog modeling of nonlinear audio circuits. S5.3. MI-GEN\(\sim\): An Efficient and Accessible Mass-Interaction Sound Synthesis Toolbox  
[PERSON] and [PERSON]  
Physical modelling techniques are now an essential part of digital sound synthesis, allowing for the creation of complex timbres through the simulation of virtual matter and expressive interaction with virtual vibrating bodies. However, placing these tools in the hands of the composer or musician has historically posed challenges in terms of (a) the computational expense of most real-time physically-based synthesis methods, (b) the difficulty of implementing these methods into modular tools that allow for the intuitive design of virtual instruments, without expert physics and/or computing knowledge, and (c) the generally limited access to such tools within popular software environments for musical creation. To this end, a set of open-source tools for designing and computing mass-interaction networks for physically-based sound synthesis is presented. The audio synthesis is performed within Max/MSP using the gen\(\sim\) environment, allowing for simple model design, efficient calculation of systems containing single-sample feedback loops, as well as extensive real-time control of physical parameters and model attributes. Through a series of benchmark examples, we exemplify various virtual instruments and interaction designs. S5.4.