搜索结果: 1-8 共查到“应用数学 SOURCE SEPARATION”相关记录8条 . 查询时间(0.073 秒)
REAL-TIME TIME-FREQUENCY BASED BLIND SOURCE SEPARATION
REAL-TIME TIME-FREQUENCY BLIND SOURCE SEPARATION
2015/9/29
We present a real-time version of the DUET algorithm forthe blind separation of any number of sources using only two mixtures. The method applies when sources are Wdisjoint orthogonal, that is, when t...
REAL-TIME AUDIO SOURCE SEPARATION BY DELAY AND ATTENUATION COMPENSATION IN THE TIME DOMAIN
REAL-TIME AUDIO SOURCE SEPARATION ATTENUATION COMPENSATION TIME DOMAIN
2015/9/29
There is increased interest in using microphone arrays in a variety of audio source separation and consequently speech processing applications. In particular, small arrays of two to four microphones a...
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...
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 ...
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...