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Daniel Rudoy

  1. KARMA: Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking.

    Mon, 07/04/2011 - 15:01 — SomNambul
    Authors: Patrick J. Wolfe, Daniel Rudoy, Daryush D. Mehta
    Subjects: Applications
    Abstract

    Vocal tract resonance characteristics in acoustic speech signals are
    classically tracked using frame-by-frame point estimates of formant frequencies
    followed by candidate selection and smoothing using dynamic programming methods
    that minimize ad hoc cost functions. The goal of the current work is to provide
    both point estimates and associated uncertainties of center frequencies and
    bandwidths in a statistically principled state-space framework.

  2. Time-Varying Autoregressions in Speech: Detection Theory and Applications.

    Tue, 11/10/2009 - 16:01 — SomNambul
    Authors: Patrick J. Wolfe, Daniel Rudoy, Thomas F. Quatieri
    Subjects: Applications
    Abstract

    This article develops a general detection theory for speech analysis based on
    time-varying autoregressive models, which themselves generalize the classical
    linear predictive speech analysis framework. This theory leads to a
    computationally efficient decision-theoretic procedure that may be applied to
    detect the presence of vocal tract variation in speech waveform data.

  3. Superposition frames for adaptive time-frequency analysis and fast reconstruction.

    Wed, 11/04/2009 - 16:01 — SomNambul
    Authors: Patrick J. Wolfe, Daniel Rudoy, Prabahan Basu
    Subjects: Numerical Analysis
    Abstract

    In this article we introduce a broad family of adaptive, linear
    time-frequency representations termed superposition frames, and show that they
    admit desirable fast overlap-add reconstruction properties akin to standard
    short-time Fourier techniques. This approach stands in contrast to many
    adaptive time-frequency representations in the extant literature, which, while
    more flexible than standard fixed-resolution approaches, typically fail to
    provide efficient reconstruction and often lack the regular structure necessary
    for precise frame-theoretic analysis.

  4. Bayesian changepoint analysis for atomic force microscopy and soft material indentation.

    Thu, 10/01/2009 - 20:55 — SomNambul
    Authors: Patrick J. Wolfe, Daniel Rudoy, Shelten G. Yuen, Robert D. Howe
    Subjects: Applications
    Abstract

    Material indentation studies, in which a probe is brought into controlled
    physical contact with an experimental sample, have long been a primary means by
    which scientists characterize the mechanical properties of materials. More
    recently, the advent of atomic force microscopy, which operates on the same
    fundamental principle, has in turn revolutionized the nanoscale analysis of
    soft biomaterials such as cells and tissues.

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