SPS Feed

The acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion is a natural end-to-end (E2E) system directly targeting word as output unit. Two issues exist in the system: first, the current output of the CTC model relies on the current input and does not account for context weighted inputs. This is the hard alignment issue. 

A sound field reproduction method based on the spherical wavefunction expansion of sound fields is proposed, which can be flexibly applied to various array geometries and directivities. First, we formulate sound field synthesis as a minimization problem of some norm on the difference between the desired and synthesized sound fields, and then the optimal driving signals are derived by using the spherical wavefunction expansion of the sound fields.

We address a robust detection problem for MIMO radars in Gaussian noise with unknown covariance matrix, for the mismatched case where the nominal transmit (or receive) steering vector may not be aligned with the true transmit (or receive) steering vector. Subspace models are adopted for taking into account these mismatches.

Nonlinear static multiple-input multiple-output (MIMO) systems are analyzed. The matrix formulation of Bussgang's theorem for complex Gaussian signals is rederived and put in the context of the multivariate cumulant series expansion. The attenuation matrix is a function of the input signals’ covariance and the covariance of the input and output signals.