## Abstracts
### S5. Oral Session 5: Sound Synthesis and Analysis
Combining Texture-Derived Vibrotactile Feedback, Concatenative Synthesis, and Photogrammetry for Virtual Reality Rendering  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]  
This paper describes a novel framework for real-time sonification of surface textures in virtual reality (VR), aimed towards realistically representing the experience of driving over a virtual surface. A combination of capturing techniques of real-world surfaces are used for mapping 3D geometry, texture maps, or auditory attributes (aural and vibrotactile) feedback. For the sonification rendering, we propose the use of information from primarily graphical texture features, to define target units in concatenative sound synthesis. To foster models that go beyond the current generation of simple sound textures (e.g., wind, rain, fire), towards highly \"synchronized\" and expressive scenarios, our contribution draws a framework for the higher-level modeling of a bicycle's kinematic rolling on ground contact, with enhanced perceptual symbiosis between auditory, visual, and vibrotactile stimuli. We scanned two surfaces represented as texture maps, consisting of different features, morphology, and matching navigation. We define target trajectories in a two-dimensional audio feature space, according to a temporal model and morphological attributes of the surfaces. This synthesis method serves two purposes: A real-time auditory feedback, and vibrotactile feedback induced through playing back the concatenated sound samples using a vibrotactile inducer speaker. S5.5. Percussion Synthesis using Loopback Frequency Modulation Oscillators  
[PERSON] and [PERSON]  
In this work, we apply recent research results in loopback frequency modulation (FM) to real-time parametric synthesis of percussion sounds. Loopback FM is a variant of FM synthesis whereby the carrier oscillator \"loops back\" to serve as a modulator of its own frequency. Like FM, more spectral components emerge, but further, when the loopback coefficient is made time varying, frequency trajectories that resemble the nonlinearities heard in acoustic percussion instruments appear. Here, loopback FM is used to parametrically synthesize this effect in struck percussion instruments, known to exhibit frequency sweeps (among other nonlinear characteristics) due to modal coupling. While many percussion synthesis models incorporate such nonlinear effects while aiming for acoustic accuracy, computational efficiency is often sacrificed, prohibiting real-time use. This work seeks to develop a real-time percussion synthesis model that creates a variety of novel sounds and captures the sonic qualities of nonlinear percussion instruments. A linear modal synthesis percussion model is modified to use loopback FM oscillators, which allows the model to create rich and abstract percussive hits in real-time. Musically intuitive parameters for the percussion model are emphasized, resulting in a usable percussion sound synthesizer. S5.6. Deep Linear Autoregressive Model for Interpretable Prediction of Expressive Tempo  
[PERSON]  
Anticipating a human musician's tempo for a given piece of music using a predictable model is important for interactive music applications, but existing studies base such an anticipation on hand-crafted features. Based on recent trends in using deep learning for music performance rendering, we present an online method for multi-step prediction of the tempo curve, given the past history of tempo curves and the music score that the user is playing. We present a linear autoregressive model whose parameters are determined by a deep convolutional neural network whose input is the music score and the history of the tempo curve; such an architecture allows the machine to acquire music performance idioms based on musical contexts, while being able to predict the timing based on the user's playing. Evaluations show that our model is capable of improving the tempo estimate over a commonly-used baseline for tempo prediction by 18%.