Pseudo-multi-tap pitch filters in a low bit-rate CELP speech coder
Speech Communication
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Linear Prediction of Speech
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
A stable and efficient adaptive notch filter for direct frequencyestimation
IEEE Transactions on Signal Processing
Double-Talk-Robust Prediction Error Identification Algorithms for Acoustic Echo Cancellation
IEEE Transactions on Signal Processing
Acoustic feedback cancellation for long acoustic paths using a nonstationary source model
IEEE Transactions on Signal Processing
A Pole-Zero Placement Technique for Designing Second-Order IIR Parametric Equalizer Filters
IEEE Transactions on Audio, Speech, and Language Processing
A Novel Audio Coding Scheme Using Warped Linear Prediction Model and the Discrete Wavelet Transform
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive feedback cancellation for audio applications
Signal Processing
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While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole) LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.