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This study proposes a principled method to jointly determine the placement of acoustic sources (loudspeakers) and sensors (control points/microphones) in sound field control. The goal of this setup is to efficiently produce a sound field using multiple loudspeakers, approximately matching a target sound field over a region of interest.

The state of the art in music source separation employs neural networks trained in a supervised fashion on multi-track databases to estimate the sources from a given mixture. With only few datasets available, often extensive data augmentation is used to combat overfitting. Mixing random tracks, however, can even reduce separation performance as instruments in real music are strongly correlated. The key concept in our approach is that source estimates of an optimal separator should be indistinguishable from real source signals.

In this paper, target tracking constrained to short-term linear trajectories is explored. The problem is viewed as an extension of the matrix decomposition problem into low-rank and sparse components by incorporating an additional line constraint. The Cramer–Rao Bound (CRB) for the trajectory estimation is derived; numerical results show that an alternating algorithm which estimates the various components of the trajectory image is near optimal due to proximity to the computed CRB.

In this paper, the problem of time-varying narrowband leaked Orthogonal Frequency Division multiplexing (OFDM) channel estimation is considered. The leakage effect results from practical constraints on communication systems: finite bandwidth and block length. These practical constraints effectively render a sparse channel into a non-sparse one. The inherent low-rank structure of the received signal, determined by the number of dominant paths of the channel, is exploited.