This paper presents a special window function for a Fast Fourier Transform (FFT) based spectral modeling approach for signals consisting of sinusoids plus noise. The main new idea is to choose a time window function with a simple Fourier transform. With the knowledge of the Fourier transform of the window function we are able to extract the parameters (frequency, amplitude, and phase) of sinusoids in real-time with a digital signal processor.

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In many audio applications an appropriate spectral estimation from a signal sequence is required. A common approach for this task is the linear prediction [1] where the signal spectrum is modelled by an all-pole (purely recursive) IIR (infinite impulse response) filter. Linear prediction is commonly used for coding of audio signals leading to linear predictive coding (LPC). But also some audio effects can be created using the spectral estimation of LPC. In this paper we consider the use of LPC in a real-time system. We investigate several methods of calculating the prediction coefficients to have an almost fixed workload each sample. We present modifications of the autocorrelation method and of the Burg algorithm for a sample-based calculation of the filter coefficients as alternative for the gradient adaptive lattice (GAL) method. We discuss the obtained prediction gain when using these methods regarding the required complexity each sample. The desired constant workload leads to a fast update of the spectral model which is of great benefit for both coding and audio effects.

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This paper gives an example of an auto-regressive (AR) spectral analysis on transient musical sounds. The attack part of many musical sounds is mostly too short to be analysed by a short-time Fourier analysis, whereas this short period of time is long enough for several AR-methods. The AR-spectra obtained from short segments of signals with attack transients have a sufficiently high frequency resolution. These spectra contain more information about the evolution of a sound than a fast Fourier transform made over a small amount of samples.

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Analog compander systems have been used to suppress the perception of noise in low dynamic range analog signal storage (tape recording) and signal transmission (FM radio). Commercial compander systems have been analyzed with respect to their signal processing requirements. The general structures of single- and multiband compander systems have been implemented on a high performance audio PC workstation. Audio tests and measurements with the optimized compander algorithms and parameters show very good performance. Even for transmission channels with very low signal-to-noise ratio (SNR of only 40 dB) an optimized digital multi-band compander emulation removes the channel noise perceptively from the output signal of the transmission system.

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