Download An Adaptive Technique For Modeling Audio Signals
In many applications of audio signal processing modeling of the signal is required. The most commonly used approach for audio signal modeling is to assume the audio signal as an (autoregressive) AR-process where the audio signal is locally stationary over a relatively short time interval. In this case the audio signal can be modeled with an all-pole IIR (infinite impulse response) filter, which leads to LPC (linear predictive coding) where the current input sample is predicted by a linear combination of past samples of the input signal. However, in practice the relatively short time interval (i.e. a frame) where the signal is stationary will vary significantly in the audio signal data stream. Also the information content of the frames will show considerable variation. For a proper modeling of an audio signal it is essential that a suitable frame size and appropriate number of model parameters is used instead of a constant frame size and model order. In this paper we present an adaptive frame-by-frame technique for modeling audio signals, which automatically adjusts the optimal modeling frame size and the optimal number of model parameters for each frame.
Download Audio Signal Extrapolation - Theory and Applications
A method for extrapolating discrete audio signals is described. The theory of extrapolation is studied and some applications are presented and demonstrated. The extrapolation method is fast and capable of extrapolating several thousand samples of CD-quality audio signals. The extrapolation is applied in practice to enhance the spectral resolution in short-time fast Fourier transform based methods. It is also applied to eliminate impulsive noise bursts and to recover missing signal sections.
Download Interpolation of long gaps in audio signals using the warped Burg's method
This paper addresses the reconstruction of missing samples in audio signals via model-based interpolation schemes. We demonstrate through examples that employing a frequency-warped version of Burg’s method is advantageous for interpolation of long duration signal gaps. Our experiments show that using frequencywarping to focus modeling on low frequencies allows reducing the order of the autoregressive models without degrading the quality of the reconstructed signal. Thus a better balance between qualitative performance and computational complexity can be achieved.
Download Exponential Weighting Method for Sample-by-Sample Update of Warped AR-Model
Auto-regressive (AR) modeling is a powerful tool having many ap­ plications in audio signal processing. The modeling procedure can be focused to low or high frequency range using frequency warp­ ing. Conventionally the AR-modeling procedure is accomplished with frame-by-frame processing which introduces latency. As with any frame-by-frame algorithm full frame has to be available for the algorithm before any output can be produced. This latency makes AR-modeling more or less unusable in real-time sound effects es­ pecially when long frame lengths are required. In this paper we introduce an exponential weighting (EW) method for sample-bysample update of the warped AR-model. This method reduces the latency down to the order of the AR-model.