Using a recently developed real time audio camera, that uses the output of a spherical microphone array beamformer steered in all directions to create central projection to create...
Adam O'Donovan, Ramani Duraiswami, Dmitry N. Zotki...
The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles ...
Rui M. Castro, Jarvis Haupt, Robert Nowak, Gil M. ...
The emerging theory of compressed sensing (CS) provides a universal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection...
We propose new algorithms for estimating autoregressive (AR), moving average (MA), and ARMA models in the spectral domain. These algorithms are derived from a maximum likelihood a...
This paper describes unsupervised speech/speaker cluster validity measures based on a dissimilarity metric, for the purpose of estimating the number of clusters in a speech data s...
Kuntoro Adi, Kristine E. Sonstrom, Peter M. Scheif...
The theory of compressive sensing has shown that sparse signals can be reconstructed exactly from many fewer measurements than traditionally believed necessary. In [1], it was sho...
In design of experiments for nonlinear regression model identification, the design criterion depends on the unknown parameters to be identified. Classical strategies consist in ...
H. ElAbiad, Laurent Le Brusquet, Marie-Eve Davoust
Corpus-based concatenative speech synthesis is very popular these days due to its highly natural speech quality. The amount of computation required in the run time, however, is of...