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Reconstruction of missing features promotes robustness in speaker recognition applications under noisy conditions. In this paper, we aim at enhancing the reliability of speech features for noise robust speaker identification under short training and testing sessions restrictions. Towards this direction, we apply a low-rank matrix recovery approach to reconstruct the unreliable spectrographic data...
In this work we propose a grid-based method to estimate the location of multiple sources in a wireless acoustic sensor network, where each sensor node contains a microphone array and only transmits direction-of-arrival (DOA) estimates in each time interval, minimizing the transmissions to the central processing node. We present new work on modeling the DOA estimation error in such a scenario. Through...
The application of Compressive Sensing is explored in three signal categories; footstep sounds, hand tremors and speech. An investigation of the reconstruction performance of various dictionaries is undertaken. It is demonstrated that these signal categories are reconstructed with higher SNR performance using K-SVD dictionaries than other fixed dictionaries. In particular, for footstep sounds and...
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