Fractal modulation is obtained by forming a power weighted superposition of scaled and modulated versions of the signal. The resulting signal is self-similar with fractal characteristics. In this paper we explore fractal modulation as a powerful method to generate rich signals, useful both for the synthesis of complex sounds, like the sounds from natural events or ecological sounds, or as control functions of audio effects. The wavelet transform can be used as an efficient tool in order to generate a subset of fractal modulated signals that are power homogeneous. Any signal used as a seed for fractal modulation is transformed into a multiscale sound by means of a tree-structured multirate filter bank. Moreover, by superimposing a structured modulation scheme one can generate pseudo-periodic sounds whose partials have fractal behavior.

Deep Brain Stimulation (DBS) is a surgical intervention that is known to reduce or eliminate the symptoms of common movement disorders, such as Parkinson.s disease, dystonia, or tremor. During the intervention the surgeon places electrodes inside of the patient.s brain to stimulate speci.c regions. Since these regions span only a couple of millimeters, and electrode misplacement has severe consequences, reliable and accurate navigation is of great importance. Usually the surgeon relies on fused CT and MRI data sets, as well as direct feedback from the patient. More recently Microelectrode Recordings (MER), which support navigation by measuring the electric .eld of the patient.s brain, are also used. We propose a visualization system that fuses the different modalities: imaging data, MER and patient checks, as well as the related uncertainties, in an intuitive way to present placement-related information in a consistent view with the goal of supporting the surgeon in the .nal placement of the stimulating electrode. We will describe the design considerations for our system, the technical realization, present the outcome of the proposed system, and provide an evaluation.

In this paper we present a new method for denoising audio signals. The method is based on the Singular Value Decomposition (SVD) of the frame matrix representing the signal inthe Overlap Add decomposition. Denoising is performed by modifying both the singular values, using a tapering model, and the singular vectors of the representation, using a nonlinear PDE method. The performance of the method is evaluated and compared with denoising obtained by filtering.

In this paper we approach the synthesis of the slide guitar, which is a particular play mode of the guitar where continuous tuning of the tones is achieved by sliding a metal orglass piece, the bottleneck, along thestrings on the guitar neck side. The bottleneck constitues a unilateral constraint for the string vibration.Dynamics is subject to friction, scraping, textured displacement and collisions. The presented model is physically inspired and is based on a dynamic model of friction, together with a geometrical model of the textured displacements and a model for collisions of the string with the bottlenck. These models are suitablefor implementation in a digital waveguide computational scheme for the 3D vibration of the string, where continuous pitch bending is achieved by allpass filters to approximate fractional delays, friction is captured by nonlinear state-space systems in the slide junction and textured displacements by signal injection at avariable point in the waveguide.

Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems. However, the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open. Owing to spectral analysis in feature extraction, an adaptive bands filter bank (ABFB) is presented. The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters. The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop. The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank. In ABFB, several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria. For the ease of optimization, only symmetrical bands are considered here, which still provide satisfactory results.

In this paper, a model for the interaction of the strings with the frets in a guitar or other fretted string instruments is introduced. In the two-polarization representation of the string oscillations it is observed that the string interacts with the fret in different ways. While the vertical oscillation is governed by perfect or imperfect clamping of the string on the fret, the horizontal oscillation is subject to friction of the string over the surface of the fret. The proposed model allows, in particular, for the accurate evaluation of the elongation of the string in the two modes, which gives rise to audible dynamic detuning. The realization of this model into a structurally passive system for use in digital waveguide synthesis is detailed. By changing the friction parameters, the model can be employed in fretless instruments too, where the string directly interacts with the neck surface.