Add to ORKGA robust time-varying filtering procedure for speech signals corrupted by mixed Gaussian and impulse noise is presented. It is based on the robust time-frequency distributions that can provide efficient representation of the noisy speech signals. The proposed approach has been compared with the time-varying filtering procedure based on the standard time-frequency distributions
Abstract—Most state-of-the-art filtering methods for speech enhancement require an estimate of the n...
Forensic audio recordings may contain undesired noise that can impair source identification, speech ...
A combination of two methods for signal parameter extraction is presented. One method involves singu...
concentrated time–frequency (TF) representation of nonsta-tionary signals. It may be used as an effi...
In this thesis an algorithm is presented which provides an estimate of the noise magnitude spectrum ...
Statistical model-based methods are presented for the reconstruction of autocorrelated signals in im...
An approach to speech watermarking based on the time-frequency signal analysis is proposed. As a tim...
Motivated by the existing time-frequency peak filtering (TFPF) algorithm, herein a robust time-varyi...
One of the great today's challenges in speech recognition is to ensure the robustness of the used sp...
A new general approach to building the filtering methods has been created on the basis of minimum du...
[[abstract]]Data-driven temporal filtering approaches based on a specific optimization technique hav...
The detection of signals in the presence of noise is one of the most basic and important problems en...
In this thesis work, time-frequency filtering of nonstationary signals in noise using Wigner-Ville D...
International audienceWiener filtering is one of the most widely used methods in audio source separa...
very speech recognition system requires a signal representation that parametrically models the tempo...
Abstract—Most state-of-the-art filtering methods for speech enhancement require an estimate of the n...
Forensic audio recordings may contain undesired noise that can impair source identification, speech ...
A combination of two methods for signal parameter extraction is presented. One method involves singu...
concentrated time–frequency (TF) representation of nonsta-tionary signals. It may be used as an effi...
In this thesis an algorithm is presented which provides an estimate of the noise magnitude spectrum ...
Statistical model-based methods are presented for the reconstruction of autocorrelated signals in im...
An approach to speech watermarking based on the time-frequency signal analysis is proposed. As a tim...
Motivated by the existing time-frequency peak filtering (TFPF) algorithm, herein a robust time-varyi...
One of the great today's challenges in speech recognition is to ensure the robustness of the used sp...
A new general approach to building the filtering methods has been created on the basis of minimum du...
[[abstract]]Data-driven temporal filtering approaches based on a specific optimization technique hav...
The detection of signals in the presence of noise is one of the most basic and important problems en...
In this thesis work, time-frequency filtering of nonstationary signals in noise using Wigner-Ville D...
International audienceWiener filtering is one of the most widely used methods in audio source separa...
very speech recognition system requires a signal representation that parametrically models the tempo...
Abstract—Most state-of-the-art filtering methods for speech enhancement require an estimate of the n...
Forensic audio recordings may contain undesired noise that can impair source identification, speech ...
A combination of two methods for signal parameter extraction is presented. One method involves singu...