In this work we derive a new cepstrum based maximum likelihood fundamental frequency estimator that exploits the information of multiple microphones. The new approach results in a...
In this paper, we present two new noise variance estimation methods of OFDMA signals transmitted through an unknown multipath fading channel. We focus on blind estimation as it do...
In this paper, we solve the problem of detecting the entries of a sparse finite-alphabet signal from a limited amount of data, for instance obtained by compressive sampling. While...
In this paper, we consider social peer-to-peer (P2P) networks, where peers are sharing their resources (i.e., multimedia content and upload bandwidth). In the considered P2P netwo...
In narrowband multiple-input multiple-output (MIMO) communication systems, when the channel state information (CSI) is known perfectly at the transmitter and the receiver, techniq...
In this paper we propose a new set of parameters for audio signal analysis and classification. These parameters are regressions computed on the normalized modulation spectrum of h...
With the widespread popularity of digital images and the presence of easy-to-use image editing software, content integrity can no longer be taken for granted, and there is a stron...
Monaural speech segregation in reverberant environments is a very difficult problem. We develop a supervised learning approach by proposing an objective function that directly rel...
Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative t...
We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals...