  Single-channel speech separation has recently made great progress thanks to
learned filterbanks as used in ConvTasNet. In parallel, parameterized
filterbanks have been proposed for speaker recognition where only center
frequencies and bandwidths are learned. In this work, we extend real-valued
learned and parameterized filterbanks into complex-valued analytic filterbanks
and define a set of corresponding representations and masking strategies. We
evaluate these filterbanks on a newly released noisy speech separation dataset
(WHAM). The results show that the proposed analytic learned filterbank
consistently outperforms the real-valued filterbank of ConvTasNet. Also, we
validate the use of parameterized filterbanks and show that complex-valued
representations and masks are beneficial in all conditions. Finally, we show
that the STFT achieves its best performance for 2ms windows.
