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Sunday, October 12 2:00 pm – 6:00 pm
Session M Signal Processing for Audio, Part 2

M-1 Diffuse Field Reverberation Modeled as a Flat Fading ChannelAndrew Eloff, Raw Thrills, Inc., Niles, IL, USA; Gary Kendall, Michael Honig, Northwestern University, Evanston, IL, USA
Artificial reverberation continues to be a source of much research and development. Currently, there is a heavy emphasis on “auralization,” or the ability to simulate physical structures' sound characteristics using computer modeling. The diffuse component of early-order reflections has been acknowledged as an important component of reverberation for several decades and has been implemented since the 1970s in varying forms. The current paper details a method of simulating diffuse reflections by use of fading models commonly used in wireless communications. The method is then considered as a stereo field enhancement effect and as a component of a reverberation system. Both are implemented in MATLAB and the results are discussed.

M-2 Intelligent Class D Amplifier Controller Integrated Circuit as an Ingredient Technology for Multichannel Amplifier Modules of Greater than 50 Watts/ChannelSteven Harris, Jack Andersen, Daniel Chieng, D2Audio Corporation, Austin, TX, USA
Digital input class D audio amplifiers will replace traditional analog types over the next five years. This paper describes a digital input class D amplifier controller integrated circuit which performs many of the functions needed to build a high performance class D audio amplifier module. A powerful DSP is included allowing sophisticated modulation schemes, as well as additional audio signal processing. Possible processing functions include loudspeaker load compensation, EQ, time alignment, room acoustics compensation, howl prevention, and other audio signal processing tasks. A novel clocking scheme decouples the input clock from the output switching clock, creating a highly jitter-tolerant design.

M-3 High Quality Multichannel Time-Scaling and Pitch-Shifting Using Auditory Scene AnalysisBrett Crockett, Dolby Laboratories, San Francisco, CA, USA
A method of using auditory scene analysis of audio signals in conjunction with time and pitch scaling is presented. In the method described, a multichannel audio signal is analyzed and the location and duration of the individual audio signal components that correspond to distinct auditory scene elements are identified. The audio data is then time and/or pitch scaled in such a way that the separate audio signal components are processed individually, thereby greatly reducing audible artifacts inherent in time and pitch scaling processing.

M-4 Adaptive Digital Calibration of Over-Sampled Data Converter SystemsThomas Holm Hansen, Texas Instruments Denmark, Copenhagen NV, Denmark; University of Copenhagen, Copenhagen NV, Denmark; Lars Risbo, Texas Instruments Denmark, Copenhagen NV, Denmark
A novel digital-domain adaptive calibration technique is proposed, which compensates for analog-related errors in over-sampled data converter systems. The technique is suited for all types of over-sampled A/D and D/A converters, e.g., multibit, 1-bit, PWM, etc. The calibration is done by adaptive fitting of a digital error model to the physical errors due to component mismatch, etc.

M-5 Efficient Algorithms for Look-Ahead Sigma-Delta ModulatorsJames Angus, University of Salford, Salford, Greater Manchester, UK
Trellis Noise-Shaping Sigma-Delta modulators look forward at k samples of the signal before deciding to output a “one” or a “zero.” The Viterbi algorithm is then used to search the trellis of the exponential number of possibilities that such a procedure generates. Means of making the search more computationally efficient have been proposed. This paper describes alternative tree based algorithms that can also be used to search the exponential number of possibilities generated by look-ahead noise-shaping S-D modulators. Tree based algorithms are simpler to implement because they do not require backtracking through an array of scores to determine the correct output value. They can also be made more efficient via the use of the “Fano” or “Stack” algorithms, which are described.

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