The use of a parameterized encoders or audio front-ends has shown promises in improving the interpretability of time domain single-channel source separation models such as Conv-TasNet. This type of filters also allows a potential reduction of the computational cost since larger encoder filters can be used. In this work, we propose to build a new parameterization of such encoder filter-bank which allows gaining interpretability while keeping flexibility. Based on the Hilbert transform and the Bedrosian theorem, we propose to build phase-shifted set of filters by modulating sinusoids through freely learned low pass filters. We show that the use of these filters allows to keep the same performances when using small filters and even improve them when using large filters.