DS-2000 Frequently Asked Questions

The DS-0230 time-frequency analysis software is designed to perform computational processing on time-axis data (file extensions: .dat/.prn) recorded by each software in the DS-2000 series and our FFT analyzers (such as the CF-6400/CF-5000/CF-4200 series), executing operations like short-time Fourier transform, wavelet transform, and Wigner distribution analysis.

Wigner distribution

The Wigner distribution is an analytical method that allows for the simultaneous analysis of temporal variations and spatial transitions of complex waveforms such as sudden or non-stationary sounds and vibrations. Proposed by E. Wigner in 1932 in the field of quantum mechanics, it gained attention as a means of analyzing non-stationary signals after being applied to acoustic analysis by T. Classen and W. Mecklenbraker. The Wigner distribution, which has dimensions of energy, offers the highest resolution in both time and frequency, enabling it to capture the characteristics of transient signals better than traditional methods. However, it often presents negative energy and cross terms, necessitating specialized interpretation. By utilizing the time-frequency distribution of non-stationary signals provided by the Wigner distribution function, it is expected to become an effective tool for analyzing shock sounds, abnormal noises, and the transient characteristics of acoustic devices.

Wavelet transform

Wavelet transform is a new analytical method that allows for the simultaneous analysis of temporal variations and spatial transitions in complex waveforms such as sudden or non-stationary sounds and vibrations. Initially introduced by petroleum exploration engineers as a tool for analyzing artificial seismic waves, wavelet transform has been developed through the efforts of physicists, mathematicians, and engineers, who have attempted to apply it across various fields based on its mathematical foundations. As the name 'wavelet' (localized small wave) suggests, it uses a single function that is localized both temporally and frequency-wise (in practical terms), applying scale and shift transformations to obtain a set of functions used as basis functions. This natural analytical method employs long time data for low frequencies (slow variations) and short time data for high frequencies (rapid variations), making it a notable tool for analyzing various transient phenomena. The distribution of the absolute square values of the wavelet transform results is referred to as a scalogram. The DS-0230 time-frequency analysis software can display the signal's spectrum on a time-frequency plane as a two-dimensional or three-dimensional color image.

STFT