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BLIND SOURCE SEPARATION BASED ON SPACE-TIME-FREQUENCY DIVERSITY
BLIND SOURCE SEPARATION SPACE-TIME-FREQUENCY DIVERSITY
2015/9/29
We investigate the assumption that sources have disjoint support in the time domain, time-frequency domain, or frequency domain. We call such signals disjoint orthogonal. The class of signals that app...
SCALABLE NON-SQUARE BLIND SOURCE SEPARATION IN THE PRESENCE OF NOISE
SCALABLE NON-SQUARE SOURCE SEPARATION PRESENCE OF NOISE
2015/9/29
Few source separation and independent component analysis approaches attempt to deal with noisy data. Weconsider an additive noise mixing model with an arbitrary number of sensors and possibly more sou...
NON-SQUARE BLIND SOURCE SEPARATION UNDER COHERENT NOISE BY BEAMFORMING AND TIME-FREQUENCY MASKING
NON-SQUARE BLIND SOURCE SEPARATION COHERENT NOISE BY BEAMFORMING TIME-FREQUENCY MASKING
2015/9/29
To be applicable in realistic scenarios, blind source separation approaches should deal evenly with non-square cases and the presence of noise. We consider an additive noisemixing model with an arbitr...
MULTI-CHANNEL PSYCHOACOUSTICALLY MOTIVATED SPEECH ENHANCEMENT
MULTI-CHANNEL PSYCHOACOUSTICALLY SPEECH ENHANCEMENT
2015/9/29
Multichannel techniques offer advantages in noise reduction and overall output signal quality when compared to the well studied mono approaches. In this paper we present an original multichannel psych...
MINUET:MUSICAL INTERFERENCE UNMIXING ESTIMATION TECHNIQUE
MINUET MUSICAL INTERFERENCE UNMIXING ESTIMATION TECHNIQUE
2015/9/29
We propose a noise cancellation technique that performs robustly in the presence of poor channel estimates and channel synchronization errors. The technique is based on the assumption that the signals...
GENERALIZED SPARSE SIGNAL MIXING MODEL AND APPLICATION TO NOISY BLIND SOURCE SEPARATION
GENERALIZED SPARSE SIGNAL MIXING MODEL NOISY BLIND SOURCE SEPARATION
2015/9/29
Sparse constraints on signal decompositions are justified bytypical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to ...
BAYESIAN SINGLE CHANNEL SPEECH ENHANCEMENT EXPLOITING SPARSENESS IN THE ICA DOMAIN
BAYESIAN SINGLE CHANNEL SPEECH ENHANCEMENT EXPLOITING SPARSENESS ICA DOMAIN
2015/9/29
We propose a Bayesian single channel speech enhancement algorithm to exploit speech sparseness in the independent component analysis (ICA) domain. While recent literature considers the idea of denoisi...
Comparison of Wavelet and FFT Based Single Channel Speech Signal Noise Reduction Techniques
DWT DWPT wavelet wavelet packet FFT noise control speech enhancement noise cancellation filter
2015/9/29
This paper compares wavelet and short time Fourier transform based techniques for single channel speechsignal noise reduction. Despite success of wavelet denoising of images, it has not yet been widel...
TIME-FREQUENCY AND TIME-SCALE CANONICAL REPRESENTATIONS OF DOUBLY SPREAD CHANNELS
TIME-FREQUENCY TIME-SCALE CANONICAL REPRESENTATIONS DOUBLY SPREAD CHANNELS
2015/9/29
A general technique for the generation of canonical channel models and demonstrate the application of the technique to time-frequency and time-scale integral kernel operators is developed. As an examp...
Speaker Verification Using Orthogonal GMM with Fusion of Threshold,Identification Front-end,and UBM
Speaker Verification Orthogonal GMM Fusion of Threshold Identification Front-end UBM
2015/9/29
This paper shows that the performance of a Gaussian Mixture Model using a Universal Background Model (GMM-UBM) speaker verification (SV) system can be further improved by combining it with threshold a...
Statistical Signal Processing for Novelty Detection
Support Vector Machines Health condition monitoring Novelty detection and Machine learning methods
2015/9/29
The goal of this article is to investigate and suggest techniques for health condition monitoring and diagnosis using machine learning from sensor data. In particular, this article overview and discus...
Convolutive Demixing with Sparse Discrete Prior Models for Markov Sources
Convolutive Demixing Sparse Discrete Prior Models Markov Sources
2015/9/29
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with...
SOURCE SEPARATION USING SPARSE DISCRETE PRIOR MODELS
SOURCE SEPARATION SPARSE DISCRETE PRIOR MODELS
2015/9/29
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variablewith ...
MAP SOURCE SEPARATION USING BELIEF PROPAGATION NETWORKS
MAP SOURCE SEPARATION BELIEF PROPAGATION NETWORKS
2015/9/29
In this paper we continue our treatment of source separation based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable...
SPEECH NOISE ESTIMATION USING ENHANCED MINIMA CONTROLLED RECURSIVE AVERAGING
noise power spectrum estimation noise control speech enhancement noise cancellation filter
2015/9/29
Accurate noise power spectrum estimation in a noisy speech signal is a key challenge problem in speech enhancement.One state-of-the-art approach is the minima controlledrecursive averaging (MCRA). Thi...