Robust Estimation of Non-Stationary Noise Power Spectrum for Speech Enhancement

International audienceWe propose a novel method for noise power spectrum estimation in speech enhancement. This method called extended-DATE (E-DATE) extends the d-dimensional amplitude trimmed estimator (DATE), originally introduced for additive white gaussian noise power spectrum estimation, to the more challenging scenario of non-stationary noise. The key idea is that, in each frequency bin and within a sufficiently short time period, the noise instantaneous power spectrum can be considered as approximately constant and estimated as the variance of a complex gaussian noise process possibly observed in the presence of the signal of interest. The proposed method relies on the fact that the Short-Time Fourier Transform (STFT) of noisy speech signals is sparse in the sense that transformed speech signals can be represented by a relatively small number of coefficients with large amplitudes in the time-frequency domain. The E-DATE estimator is robust in that it does not require prior information about the signal probability distribution except for the weak-sparseness property. In comparison to other state-of-the-art methods, the E-DATE is found to require the smallest number of parameters (only two). The performance of the proposed estimator has been evaluated in combination with noise reduction and compared to alternative methods. This evaluation involves objective as well as pseudo-subjective criteria

High-Order Adaptive Synchrosqueezing Transform

The prevalence of the separation of multicomponent non-stationary signals across many elds of research makes this concept an important subject of study. The synchrosqueezing transform (SST) is a particular type of reassignment method. It aims to separate and recover the components of a multicomponent non-stationary signal. The short time Fourier transform (STFT)-based SST (FSST) and the continuous wavelet transform (CWT)based SST (WSST) have been used in engineering and medical data analysis applications. The current study introduces the dierent versions of FSST and WSST to estimate instantaneous frequency (IF) and to recover components. It has a good concentration and reconstruction for a wide variety of amplitude and frequency modulated multicomponent signals. Earlier studies have improved existing FSSTs by computing more accurate estimates of the IFs of the modes making up the signal. The higher order approximations for both the amplitude and phase were used. Therefore, there is a better concentration and reconstruction for a wider variety of AM-FM modes than what was possible with current synchrosqueezing techniques. In this study, we propose to improve the adaptive FSST, the adaptive WSST, and to introduce a new type of 2nd-order FSST with a new phase transformation. We use higher order approximations for both the amplitude and phase function. We study the higher order adaptive FSST and adaptive WSST. The result shows an even better concentration and reconstruction for a wider variety of AM-FM modes with the higher order adaptive SSTs. We also study the theoretical analysis of the 2nd-order FSST with a new phase transformation. The new phase transformation introduced by us is much simpler than the convectional one, while the performance in IF estimation and component recovery of the new 2nd-order FSST is comparable with, and even better in some cases than, that of the conventional 2nd-order FSST

The cross-frequency mediation mechanism of intracortical information transactions

In a seminal paper by von Stein and Sarnthein (2000), it was hypothesized that "bottom-up" information processing of "content" elicits local, high frequency (beta-gamma) oscillations, whereas "top-down" processing is "contextual", characterized by large scale integration spanning distant cortical regions, and implemented by slower frequency (theta-alpha) oscillations. This corresponds to a mechanism of cortical information transactions, where synchronization of beta-gamma oscillations between distant cortical regions is mediated by widespread theta-alpha oscillations. It is the aim of this paper to express this hypothesis quantitatively, in terms of a model that will allow testing this type of information transaction mechanism. The basic methodology used here corresponds to statistical mediation analysis, originally developed by (Baron and Kenny 1986). We generalize the classical mediator model to the case of multivariate complex-valued data, consisting of the discrete Fourier transform coefficients of signals of electric neuronal activity, at different frequencies, and at different cortical locations. The "mediation effect" is quantified here in a novel way, as the product of "dual frequency RV-coupling coefficients", that were introduced in (Pascual-Marqui et al 2016, http://arxiv.org/abs/1603.05343). Relevant statistical procedures are presented for testing the cross-frequency mediation mechanism in general, and in particular for testing the von Stein & Sarnthein hypothesis.Comment: https://doi.org/10.1101/119362 licensed as CC-BY-NC-ND 4.0 International license: http://creativecommons.org/licenses/by-nc-nd/4.0

Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform

The Hilbert-Huang Transform is a novel, adaptive approach to time series analysis that does not make assumptions about the data form. Its adaptive, local character allows the decomposition of non-stationary signals with hightime-frequency resolution but also renders it susceptible to degradation from noise. We show that complementing the HHT with techniques such as zero-phase filtering, kernel density estimation and Fourier analysis allows it to be used effectively to detect and characterize signals with low signal to noise ratio.Comment: submitted to PRD, 10 pages, 9 figures in colo

Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay¿s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.Postprint (author's final draft

Spectral analysis for nonstationary audio

A new approach for the analysis of nonstationary signals is proposed, with a focus on audio applications. Following earlier contributions, nonstationarity is modeled via stationarity-breaking operators acting on Gaussian stationary random signals. The focus is on time warping and amplitude modulation, and an approximate maximum-likelihood approach based on suitable approximations in the wavelet transform domain is developed. This paper provides theoretical analysis of the approximations, and introduces JEFAS, a corresponding estimation algorithm. The latter is tested and validated on synthetic as well as real audio signal.Comment: IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, In pres