Steven Trautmann

Wavetable music synthesis for multimedia and beyond

Music production is a key element of any complete multimedia system and therefore an important component of multimedia chip set solution. Wavetable synthesis is a set of techniques which allow real time music production with high polyphony on current DSPs while exceeding the quality of simple FM synthesis. However it is important to allow other synthesis techniques to further enhance the musical product. Some of these techniques, such as spectral modeling synthesis and physical modeling of musical instruments can produce higher quality than wavetable but are more expensive computationally. Thus even when real-time performance is possible, there exists a trade-off between polyphony and quality. In order to provide superior quality as well as flexibility and scaleability, we have created an overall structure that can utilize various synthesis techniques in different situations with minimum overhead. Synthesis techniques such as FM, wavetable, spectral and physical modeling and so on will thus be used together to create a more compelling musical output. This is achieved with a control structure that dynamically chooses and controls the different synthesis techniques. The first set of synthesis methods incorporated in this system is a combination of wavetable and sampling techniques.

Head-related transfer function modeling in 3-D sound systems with genetic algorithms

Head-related transfer functions (HRTFs) describe the spectral filtering that occurs between a source sound and the listeners eardrum. Since HRTFs vary as a function of the relative source location and subject, practical implementation of 3D audio must take into account a large set of HRTFs for different azimuths and elevations. Previous work has proposed several HRTF models for data reduction. This paper describes our work in applying genetic algorithms to find a set of HRTF basis spectra, and the normal equation method to compute the optimal combination of linear weights to represent the individual HRTFs at different azimuths and elevations. The genetic algorithm selects the basis spectra from the set of original HRTF amplitude responses, using an average relative spectral error as the fitness function. Encouraging results from the experiments suggest that genetic algorithms provide an effective approach to this data reduction problem.

Improved sound localization employing modified wavefront reconstruction

Audio wavefront reconstruction is similar to visual holography in which optical wavefronts are reconstructed by reproducing phases and amplitudes to create the impression of an object being present when it is not there. In audio there are far fewer speakers than necessary to exactly reproduce a wavefront this way. In order to construct the closet possible approximation to a desired wavefront, information about the listening environment such as the number, placement and qualities of the loudspeakers and the nature of local reverberation can be used. The perceived quality of this approximation can be improved by using psychoacoustical properties of sound localization, such as treating amplitude as more vital than phase, ear, and head filtering, precedence effects and masking. Thus despite limited numbers of loudspeakers, improvements can be made in sound localization for quadraphonic and stereo systems, especially at low frequencies. This is done by using signal processing techniques including partial inver...