Empirical mode decomposition for adaptive AM-FM analysis of speech: A review
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 …
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 …
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
A typical large building contains thousands of sensors, monitoring the HVAC system,
lighting, and other operational sub-systems. With the increased push for operational …
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 …
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
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 …
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 …
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 …
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 …
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
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) …
communication but has become an important aspect of Human Computer Interaction (HCI) …
The inner structure of empirical mode decomposition
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 …
good localization property in the time domain for analyzing non-stationary complex data …