A generalized synchrosqueezing transform for enhancing signal time-frequency representation

An ameliorated synchroextracting transform based on upgraded local instantaneous frequency approximation

A local entropy-based algorithm for information content extraction from time–frequency distributions of noisy signals

This paper proposes an automatic method for extraction of useful information content from time-frequency distributions of nonstationary signals heavily corrupted by additive noise. The proposed method, which does not require prior knowledge of the signal, initially performs a one-dimensional clustering of the time-frequency distribution aimed at segmenting it into a fixed number of classes. This procedure points out basic structural differences of noise and signal components (i.e., useful information) in the time-frequency plane. In fact, noise presents a flat spectrum with large time-frequency supports, while signal components are narrow energy ridges. The time-frequency supports are estimated for each of the obtained classes and used as input for an algorithm aimed at discriminating “noise” affine classes, containing mainly non-zero coefficients located outside the components time-frequency supports, from “useful information” affine classes, containing mainly coefficients inside time-frequency supports of the signal components. Simulations show the superiority of the proposed algorithm, mainly in terms of error rate reduction of classified time-frequency coefficients, compared to hard amplitude thresholding methods, as well as to a recently proposed method for information content extraction based on adaptive thresholding, which is also outperformed in terms of computational complexity and execution time (by up to 43 and 28 times, respectively).

Extraction of Useful Information Content From Noisy Signals Based on Structural Affinity of Clustered TFDs' Coefficients

We develop an algorithm, the joint time-frequency segmentation algorithm, where the wavelet packet coefficients of the analyzed speech signal are represented as tiles of a time-frequency representation adapted to the characteristics of the signal itself. Further, our algorithm enables the decomposition of the speech signal into transient and non-transient components, respectively. Any block of wavelet packet coefficients, whose tiling height is larger than or equal to the tiling width belongs to the transient component and vice versa for the non-transient component. The transient component is selectively amplified and recombined with the original speech to generate the modified speech with energy adjusted to be equal to the original speech. The intelligibility of the original and modified speech is evaluated by 16 human listeners. Word recognition rate results show that the modified speech significantly improves speech intelligibility in background noise, i.e., by 10% absolute at 0 dB to 27% absolute at -30 dB.

Joint Time–Frequency Segmentation Algorithm for Transient Speech Decomposition and Speech Enhancement

We present an algorithm to decompose speech into transient and non-transient components. Our algorithm, the joint timefrequency segmentation algorithm, uses the wavelet packet coefficients of the speech signal and represents them as tiles of a time-frequency representation adapted to the characteristics of the signal itself. Any wavelet packet coefficient, whose tiling height is larger than or equal to the tiling width is characterized as a transient coefficient and vice versa for the nontransient coefficient. The transient component is selectively amplified and recombined with the original speech to generate the modified speech with energy adjusted to be equal to the energy of the original speech. The psychoacoustic tests performed with fourteen human listeners show that the speech modification significantly improves speech intelligibility in background noise, i.e., for 10% absolute at 0dB to 31% absolute at −30dB.

Speech enhancement based on joint time-frequency segmentation

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Joint Time–Frequency Segmentation Algorithm for Transient Speech Decomposition and Speech Enhancement

Speech enhancement based on joint time-frequency segmentation