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 specific 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 field 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 final 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.

Physically inspired sound synthesis techniques have been devised for several instruments including guitar. Less well studied are methods to model the interactions of the player with strings and of the strings with other mechanical parts of the instrument. In this paper, we present methods based on simple scattering junctions to be inserted at various points along a digital waveguide simulating transversal wave propagation along a string. These junctions, which are derived using a balanced perturbation method for displacement wave variables, connect the waveguide with the external stimuli provided by the player or with objects that are part of the instrument. A model for plucking is revisited and improved. New nonlinear structures for the accurate model of collisions are developed. The proposed junctions achieve realistic synthesis of the interaction of the plucking finger or pick with the string, of the interaction of the fingers on the neck-side hand with strings, such as in the production of harmonics, and of the strings themselves with frets or fingerboard in a fretless instrument.

In this paper, we propose a binaural source localization method based on interaural time differences (ITDs) and interaural level differences (ILDs). The two cues, computed from a two-channel time-frequency representation, are combined in order to estimate the azimuth of sources in binaural recording. We introduce an individual parametric model for the ITD and ILD, and an average parametric model that sets us free from measurements of the subjects' HRIRs for sound localization. We conduct several experiments to validate the proposed approach and show that it compares favorably with available techniques.

In a situation where multiple sound sources are concurrently active, the signals of the individual sources often overlap in time and in frequency. This is particularly likely for voiced instruments where the frequencies of some of the partials of one single note coincide with the frequencies of some of the partials of another instrument playing a harmonically related note. A source separation algorithm suitable for musical applications must address the problem of overlapping partials. A method is proposed for the separation of overlapping narrow-band partials in multichannel mixtures. The method is based on the observation that, for many instruments, all the partials of a single note have similar temporal envelopes. For narrow band partials these similarities can be exploited in order to estimate demixing matrices in the frequency domain. Effectively, one can recover estimates of the original partials from a multichannel mixture where they overlap. The method is computationally efficient in that it works on highly downsampled narrow frequency bands. It performs well for closely spaced and colliding partials, and (to some extent) also for frequency modulations such as vibrato effects.

In this paper we consider an extension of the wavelet transform leading to the construction of wavelets with arbitrary bandwidth. The new wavelets are complete, orthonormal and dyadic; nevertheless their bandwidth is not constrained to be one octave, rather it may be designed by selecting a set of parameters. The construction of the new bases starts in the discrete-time domain, exploiting properties of the Laguerre transform. Furthermore, we provide a procedure to define continuous-time warped wavelets. The flexibility of the bandwidth allocation allows for more and improved applications of the wavelet transform, such as signal coding, the design of auditory model based filterbanks and transient detection in pseudoperiodic signals, pointed out in the paper.

We show that the dyadic wavelet transform may be generalized to include non-octave spaced frequency resolution. We introduce orthogonal and complete wavelets whose set of cutoff frequencies may be adapted, in the simplest case, by changing a single parameter. The novel wavelets and the FWWT transform computational structure are obtained via an intermediate Laguerre representation of the signal. The warped wavelets are related to the ordinary wavelets by means of frequency transformations and orthogonalizing filtering. The classical sampled filter bank theory is extended to include frequency dependent upsampling and downsampling operators and dispersive delay lines. The FWWT frequency band flexibility may be exploited in order to adapt the wavelet transform to signals.