Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

Real-Time Iterative Spectrum Inversion with Look-Ahead

Abstract: In this paper, we present an algorithm for Real-time Iterative Spectrogram Inversion (RTISI) with Look-Ahead (RTISI-LA). RTISI-LA reconstructs a time-domain signal from a given sequence of short-time Fourier transform magnitude (STFTM) spectra without phase information. Whereas RTISI [1] reconstructs the current frame using only magnitude spectra information for previous frames and the current frame, RTISI-LA also uses magnitude spectra for a small number future frames. This allows RTISI-LA to achieve substantially higher signal-to-noise (SNR) performance than either RTISI or the Griffin & Lim method [2][3] with an equivalent computational load, while retaining the real-time properties of RTISI.

Wavelets and related time-frequency techniques in Magnetic Resonance Spectroscopy.

Abstract: We survey the various applications in MRS of the wavelet transform and related time-frequency methods. For the sake of completeness, we first quickly review the mathematical tools needed.

Time-frequency analysis of heart rate variability using short-time fourier analysis.

Abstract: This study was done to introduce new parameters derived by time frequency analysis of heart rate variability data. Four simulation experiments were carried out to compare the short-time Fourier transform (STFT) analysis method to the traditional overall spectral analysis method. Sinusoidal signals were generated with identical total power in the high-frequency band, but varying time-frequency and time-amplitude information. The STFT method was also applied to heart rate variability data from the stages of normal human sleep. Data analysis included computation of the power in the high-frequency band by overall spectral analysis. The instability coefficients (ICs) of the frequency and power in the high-frequency band were derived by STFT analysis. The ICs derived by the STFT method were able to describe time-frequency and time-amplitude variations in sinusoidal signals which contained identical total power in a specified frequency range. The ICs of the frequency and power were able to differentiate variations in vagal activity between the stages of human sleep and waking. The ICs represent new parameters derived by the STFT method, and allow the detection and quantification of short-lasting time-frequency and time-amplitude variations that remain obscured by overall spectral analysis.

Joint time-frequency scattering for audio classification

Abstract: We introduce the joint time-frequency scattering transform, a time shift invariant descriptor of time-frequency structure for audio classification. It is obtained by applying a two-dimensional wavelet transform in time and log-frequency to a time-frequency wavelet scalogram. We show that this descriptor successfully characterizes complex time-frequency phenomena such as time-varying filters and frequency modulated excitations. State-of-the-art results are achieved for signal reconstruction and phone segment classification on the TIMIT dataset.