Transformations, correlation, and modeling

The parameters of the autoregression model with all poles are the Burg method.

Rearranges the input data bits in reverse order.

Comprehensive cepstral analysis.

Circular convolution modulo n.

The convolution matrix.

A data matrix for estimating the autocorrelation matrix.

Chirp Z-transformation.

Discrete cosine transform.

The matrix of the discrete Fourier transform.

Rearranges the digits of the input data in reverse order.

Decomposition of the signal into empirical modes.

The envelope of the signal.

Discrete analytical signal using the Hilbert transform.

Inverse discrete cosine transform.

Determination of continuous filter parameters based on frequency response data.

Determination of discrete filter parameters based on frequency response data.

Inverse windowed Fourier transform.

Levinson—Durbin recursion.

Conversion of linear spectral frequencies into prediction filter coefficients.

Transformation of the prediction filter polynomial into reflection coefficients.

The Prony method for designing filters.

Analysis of signals in the frequency and time-frequency domains.

Conversion of reflection coefficients into arcsine parameters.

Conversion of reflection coefficients into parameters of the logarithmic ratio of areas.

Conversion of reflection coefficients into a prediction filter polynomial.

Real kepstr and minimum-phase reduction.

A spectrogram using the windowed Fourier transform.

The windowed Fourier transform.

Decomposition by variational modes.

Two-dimensional cross-correlation function.