Download Symbolic and audio processing to change the expressive intention of a recorded music performance A framework for real-time expressive modification of audio musical performances is presented. An expressiveness model compute the deviations of the musical parameters which are relevant in terms of control of the expressive intention. The modifications are then realized by the integration of the model with a sound processing engine.
Download Radial Basis Function Networks for conversion of sound spectra In many high-level signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
Download Model-based synthesis and transformation of voiced sounds In this work a glottal model loosely based on the Ishizaka and Flanagan model is proposed, where the number of parameters is drastically reduced. First, the glottal excitation waveform is estimated, together with the vocal tract filter parameters, using inverse filtering techniques. Then the estimated waveform is used in order to identify the nonlinear glottal model, represented by a closedloop configuration of two blocks: a second order resonant filter, tuned with respect to the signal pitch, and a regressor-based functional, whose coefficients are estimated via nonlinear identification techniques. The results show that an accurate identification of real data can be achieved with less than regressors of the nonlinear functional, and that an intuitive control of fundamental features, such as pitch and intensity, is allowed by acting on the physically informed parameters of the model. 10
Download Transformation of instrumental sound related noise by means of adaptive filtering tecniques In this work we present an extension of the classic schema of a time-varying filter excited with white noise for the modeling of noise signals from musical instrument sounds. The framework used is that of statistical signal processing, and a structure that combines an Autoregressive (AR) model with an adaptive FIR filter is proposed. A combbased structure for the AR filter is used when tuned noise is to be modeled. The analysis/resynthesis schema proposed is used to perform some basic sound transformations such as time stretching, tuning and energy envelop control, and spectral processing.
Download Sound Morphing With Gaussian Mixture Models In this work a sound transformation model based on Gaussian Mixture Models is introduced and evaluated for audio morphing. To this aim, the GMM is used to build the acoustic model of the source sound, and a set of conversion functions, which rely on the acoustic model, is used to transform the source sound. The method is experimented on a set of monophonic sounds and results show that it provides promising features.
Download Acoustic rendering of particle‐based simulation of liquids in motion This paper presents an approach to the synthesis of acoustic emission due to liquids in motion. First, the models for the liquid motion description, based on a particle-based fluid dynamics representation, and for the acoustic emission are described, along with the criteria for the control of the audio algorithms through the parameters of the particles system. Then, the experimental results are discussed for a configuration representing the falling of a liquid volume into an underlying rigid container.
Download Physics-Based and Spike-Guided Tools for Sound Design In this paper we present graphical tools and parameters search algorithms for the timbre space exploration and design of complex sounds generated by physical modeling synthesis. The tools are built around a sparse representation of sounds based on Gammatone functions and provide the designer with both a graphical and an auditory insight. The auditory representation of a number of reference sounds, located as landmarks in a 2D sound design space, provides the designer with an effective aid to direct his search for new sounds. The sonic landmarks can either be synthetic sounds chosen by the user or be automatically derived by using clever parameter search and clustering algorithms. The proposed probabilistic method in this paper makes use of the sparse representations to model the distance between sparsely represented sounds. A subsequent optimization model minimizes those distances to estimate the optimal parameters, which generate the landmark sounds on the given auditory landscape.