Saturday, October 1, 3:15 pm — 4:45 pm (Rm 409B)
Christoph M. Musialik, Sennheiser Audio Labs - Waldshut-Tiengen, Germany
P22-1 Loudspeaker Crossover Network Optimizer for Multiple Amplitude Response Objectives—William Decanio, Samsung Research America - Valencia, CA, USA; Ritesh Banka, Samsung Research America - Valencia, CA USA
Even though the correlation between multi-spatial amplitude response metrics and listener preferences has been established, most commercial loudspeaker crossover optimization tools operate on only a single axis of loudspeaker system’s amplitude response. This paper describes development of a software based crossover network optimizer that is capable of simultaneously optimizing the on-axis, as well as off-axis acoustic response of a loudspeaker system. Choice of off-axis acoustic metrics as well as a general description of software and implementation of numerical optimization algorithms will be briefly discussed. Several design examples are presented, and measured versus predicted results will be shown.
Convention Paper 9669 (Purchase now)
P22-2 The Relationship between the Bandlimited Step Method (BLEP), Gibbs Phenomenon, and Lanczos Sigma Correction—Akhil Singh, University of Miami - Coral Gables, FL, USA; Will Pirkle, University of Miami - Coral Gables, FL, USA
In virtual analog synthesis of traditional waveforms, several approaches have been developed for smoothing the discontinuities in trivial waveforms to reduce or eliminate aliasing while attempting to preserve both the time and frequency domain responses of the original analog waveforms. The Bandlimited Step Method (BLEP) has been found to produce excellent results with low computational overhead. The correction scheme first starts with a sinc function—the impulse response of a low-pass filter—and uses it to generate offset values that are applied to the points around the discontinuity. This paper discusses the relationships that exist between the BLEP method, Gibbs Phenomenon, and the Lanczos Sigma correction method.
Convention Paper 9670 (Purchase now)
P22-3 Modeling and Adaptive Filtering for Systems with Output Nonlinearity—Erfan Soltanmohammadi, Marvell Semiconductor, Inc. - Santa Clara, CA, USA
Many practical systems are nonlinear in nature, and the Volterra series, also known as nonlinear convolution, is widely used to model these systems. For nonlinear systems with infinite memory, such a modeling approach is usually not feasible because of multiple infinite summations. In practice, the full Volterra series representation of such a system is either approximated by just a few terms, or is otherwise simplified. In an audio system, a useful approximation is to model all memoryless and dynamical nonlinear effects as a combined nonlinearity at its output. In this paper we propose a new Volterra-based structure that accommodates nonlinear systems with output nonlinearity and infinite memory. We then propose an adaptation approach to estimate the Volterra kernels based on the Least Mean Squares (LMS) approach.
Convention Paper 9671 (Purchase now)