Empirical mode decomposition for adaptive AM-FM analysis of speech: A review

R Sharma, L Vignolo, G Schlotthauer… - Speech …, 2017 - Elsevier
This work reviews the advancements in the non-conventional analysis of speech signals,
particularly from an AM-FM analysis point of view. The benefits of such an analysis, as …

EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

N Chatlani, JJ Soraghan - IEEE Transactions on Audio, Speech …, 2011 - ieeexplore.ieee.org
An empirical mode decomposition-based filtering (EMDF) approach is presented as a
postprocessing stage for speech enhancement. This method is particularly effective in low …

Strip, bind, and search: a method for identifying abnormal energy consumption in buildings

R Fontugne, J Ortiz, N Tremblay, P Borgnat… - Proceedings of the 12th …, 2013 - dl.acm.org
A typical large building contains thousands of sensors, monitoring the HVAC system,
lighting, and other operational sub-systems. With the increased push for operational …

A Novel Generative Adversarial Network for the Removal of Noise and Baseline Drift in Seismic Signals

Y Chen, D Ji, Q Ma, C Zhai, Y Ma - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Recorded seismic signals are of fundamental importance in earthquake engineering,
serving as vital data for analyzing and comprehending various ground motion parameters …

Voiced/unvoiced speech classification‐based adaptive filtering of decomposed empirical modes for speech enhancement

K Khaldi, AO Boudraa, M Turki - IET Signal Processing, 2016 - Wiley Online Library
This study presents a speech filtering method exploiting the combined effects of the
empirical mode decomposition (EMD) and the local statistics of the speech signal using the …

Maximum likelihood acoustic factor analysis models for robust speaker verification in noise

T Hasan, JHL Hansen - IEEE/ACM transactions on audio …, 2013 - ieeexplore.ieee.org
Recent speaker recognition/verification systems generally utilize an utterance dependent
fixed dimensional vector as features to Bayesian classifiers. These vectors, known as i …

On the estimation of fundamental frequency from nonstationary noisy speech signals based on the Hilbert–Huang transform

L Zão, R Coelho - IEEE Signal Processing Letters, 2017 - ieeexplore.ieee.org
This letter introduces a method based on the Hilbert-Huang transform (HHT) to estimate the
fundamental frequency of nonstationary noisy speech signals. For this purpose, the target …

Stress wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model

YM Fang, HL Feng, J Li, GH Li - Sensors, 2011 - mdpi.com
Stress-wave-based techniques have been proven to be an accurate nondestructive test
means for determining the quality of wood based materials and they been widely used for …

[图书][B] Phoneme-based speech segmentation using hybrid soft computing framework

M Sarma, KK Sarma - 2014 - Springer
Speech is a naturally occuring nonstationary signal essential not only for personto-person
communication but has become an important aspect of Human Computer Interaction (HCI) …

The inner structure of empirical mode decomposition

YH Wang, HWV Young, MT Lo - Physica A: Statistical Mechanics and its …, 2016 - Elsevier
The empirical mode decomposition (EMD) is a nonlinear method that is truly adaptive with
good localization property in the time domain for analyzing non-stationary complex data …