Variable Span Filters for Speech Enhancement

In this work, we consider enhancement of multichannel speech
recordings. Linear filtering and subspace approaches have been considered
previously for solving the problem. The current linear filtering
methods, although many variants exist, have limited control
of noise reduction and speech distortion. Subspace approaches, on
the other hand, can potentially yield better control by filtering in the
eigen-domain, but traditionally these approaches have not been optimized
explicitly for traditional noise reduction and signal distortion
measures. Herein, we combine these approaches by deriving optimal
filters using a joint diagonalization as a basis. This gives excellent
control over the performance, as we can optimize for noise reduction
or signal distortion performance. Results from real data experiments
show that the proposed variable span filters can achieve better performance
than existing filters. In terms of output SNR, the gain was
more than 8 dB, and more than 0.1 in mean opinion score in the
conducted experiments.