Showing papers by "Ronald W. Schafer published in 2007"

An Introduction to Digital Speech Processing

Abstract: Since even before the time of Alexander Graham Bell's revolutionary invention, engineers and scientists have studied the phenomenon of speech communication with an eye on creating more efficient and effective systems of human-to-human and human-to-machine communication. Starting in the 1960s, digital signal processing (DSP), assumed a central role in speech studies, and today DSP is the key to realizing the fruits of the knowledge that has been gained through decades of research. Concomitant advances in integrated circuit technology and computer architecture have aligned to create a technological environment with virtually limitless opportunities for innovation in speech communication applications. In this text, we highlight the central role of DSP techniques in modern speech communication research and applications. We present a comprehensive overview of digital speech processing that ranges from the basic nature of the speech signal, through a variety of methods of representing speech in digital form, to applications in voice communication and automatic synthesis and recognition of speech. The breadth of this subject does not allow us to discuss any aspect of speech processing to great depth; hence our goal is to provide a useful introduction to the wide range of important concepts that comprise the field of digital speech processing. A more comprehensive treatment will appear in the forthcoming book, Theory and Application of Digital Speech Processing [101].

System and method for canceling acoustic echoes in audio-conference communication systems

Abstract: Various embodiments of the present invention are directed to a frequency-domain coder/decoder (802) for an audio-conference communication system that includes acoustic-echo-cancellation functionality. In one embodiment of the present invention, an acoustic echo canceller (812) is integrated into the frequency-domain coder/decoder (802) and ameliorates or removes acoustic echoes from audio signals that have been transformed to the frequency domain and divided into subbands by the frequency-domain coder/decoder (802).

4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priors

Abstract: We present a 4D spatiotemporal segmentation algorithm based on the Mumford-Shah functional coupled with shape priors. When used in a clinical setting, our algorithm could greatly alleviate the time that clinicians must spend working with the acquired data to manually retrieve diagnostically meaningful measurements. The advantage of the 4D algorithm is that segmentation occurs in both space and time simultaneously, improving accuracy and robustness over existing 2D and 3D methods. The segmentation contour or hyper-surface is a zero level set function in 4D space that exploits the coherence within continuous regions not only between spatial slices, but between consecutive time samples as well. Shape priors are incorporated into the segmentation to limit the result to a known shape. Variations in shape are computed using principal component analysis (PCA), of a signed distance representation of the training data derived from manual segmentation of 18 carefully selected data sets. The automatic segmentation occurs by manipulating the parameters of this signed distance representation to minimize a predetermined energy functional. Several tests are presented to show the consistency and accuracy of the novel automatic 4D segmentation process.

Phase-Domain Statistical Analysis for Audio Source Localization

Abstract: We consider the problem of estimating the time-difference-of-arrival (TDOA) for audio source localization in noisy environments, defining a framework for statistical analysis in the phase domain which enables more reliable estimates. This is motivated by the fact that with complex sources, noise, and interfering signals, different frequency bands have significantly different signal-to-noise ratios, creating non-uniform distributions of errors in the phase measurements. Through a new method for analysis of the variations of the phase in frequency windows, we first estimate the signal-to-noise ratio for frequency, and then use it in a maximum-likelihood estimation of the time difference of arrival. We show that this corresponds to a generalization of the Phase Transform method (PHAT), and provides a theoretical justification of why it works so well. Numerical results show how the proposed technique compares favorably with PHAT.

DSP First (2nd Edition)

Abstract: 2 Sinusoids 9 2-1 Tuning-Fork Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2-2 Review of Sine and Cosine Functions . . . . . . . . . . . . . . . . . . . . 12 2-3 Sinusoidal Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2-3.1 Relation of Frequency to Period . . . . . . . . . . . . . . . . . . . 15 2-3.2 Phase and Time Shift . . . . . . . . . . . . . . . . . . . . . . . . . 17 2-4 Sampling and Plotting Sinusoids . . . . . . . . . . . . . . . . . . . . . . . 19 2-5 Complex Exponentials and Phasors . . . . . . . . . . . . . . . . . . . . . 21 2-5.1 Review of Complex Numbers . . . . . . . . . . . . . . . . . . . . 22 2-5.2 Complex Exponential Signals . . . . . . . . . . . . . . . . . . . . 23 2-5.3 The Rotating Phasor Interpretation . . . . . . . . . . . . . . . . . . 25 2-5.4 Inverse Euler Formulas . . . . . . . . . . . . . . . . . . . . . . . . 27 2-6 Phasor Addition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2-6.1 Addition of Complex Numbers . . . . . . . . . . . . . . . . . . . . 29 2-6.2 Phasor Addition Rule . . . . . . . . . . . . . . . . . . . . . . . . . 29 2-6.3 Phasor Addition Rule: Example . . . . . . . . . . . . . . . . . . . 31 2-6.4 MATLAB Demo of Phasors . . . . . . . . . . . . . . . . . . . . . 33 2-6.5 Summary of the Phasor Addition Rule . . . . . . . . . . . . . . . . 33 2-7 Physics of the Tuning Fork . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2-7.1 Equations from Laws of Physics . . . . . . . . . . . . . . . . . . . 34 2-7.2 General Solution to the Differential Equation . . . . . . . . . . . . 37 2-7.3 Listening to Tones . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2-8 Time Signals: More Than Formulas . . . . . . . . . . . . . . . . . . . . . 38 2-9 Summary and Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2-10 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40