Controlling a non linear friction model for evocative sound synthesis applications

International audienceIn this paper, a flexible strategy to control a synthesis model of sounds produced by non linear friction phenomena is proposed for guidance or musical purposes. It enables to synthesize different types of sounds, such a creaky door, a singing glass or a squeaking wet plate. This approach is based on the action/object paradigm that enables to propose a synthesis strategy using classical linear filtering techniques (source/resonance approach) which provide an efficient implementation. Within this paradigm, a sound can be considered as the result of an action (e.g. impacting, rubbing, ...) on an object (plate, bowl, ...). However, in the case of non linear friction phenomena, simulating the physical coupling between the action and the object with a completely decoupled source/resonance model is a real and relevant challenge. To meet this challenge, we propose to use a synthesis model of the source that is tuned on recorded sounds according to physical and spectral observations. This model enables to synthesize many types of non linear behaviors. A control strategy of the model is then proposed by defining a flexible physically informed mapping between a descriptor, and the non linear synthesis behavior. Finally, potential applications to the remediation of motor diseases are presented. In all sections, video and audio materials are available at the following URL: http://www.lma.cnrs-mrs.fr/~kronland/thoretDAFx2013

Nonparametric estimation of the dynamic range of music signals

The dynamic range is an important parameter which measures the spread of sound power, and for music signals it is a measure of recording quality. There are various descriptive measures of sound power, none of which has strong statistical foundations. We start from a nonparametric model for sound waves where an additive stochastic term has the role to catch transient energy. This component is recovered by a simple rate-optimal kernel estimator that requires a single data-driven tuning. The distribution of its variance is approximated by a consistent random subsampling method that is able to cope with the massive size of the typical dataset. Based on the latter, we propose a statistic, and an estimation method that is able to represent the dynamic range concept consistently. The behavior of the statistic is assessed based on a large numerical experiment where we simulate dynamic compression on a selection of real music signals. Application of the method to real data also shows how the proposed method can predict subjective experts' opinions about the hifi quality of a recording

Interactive Neural Resonators

In this work, we propose a method for the controllable synthesis of real-time contact sounds using neural resonators. Previous works have used physically inspired statistical methods and physical modelling for object materials and excitation signals. Our method incorporates differentiable second-order resonators and estimates their coefficients using a neural network that is conditioned on physical parameters. This allows for interactive dynamic control and the generation of novel sounds in an intuitive manner. We demonstrate the practical implementation of our method and explore its potential creative applications

Interactive Neural Resonators

In this work, we propose a method for the controllable synthesis of real-time contact sounds using neural resonators. Previous works have used physically inspired statistical methods and physical modelling for object materials and excitation signals. Our method incorporates differentiable second-order resonators and estimates their coefficients using a neural network that is conditioned on physical parameters. This allows for interactive dynamic control and the generation of novel sounds in an intuitive manner. We demonstrate the practical implementation of our method and explore its potential creative applications