Survival analysis involves the modelling of the times to event. Proposed neural network approaches maximise the predictive performance of traditional survival models at the cost of their interpretability. This impairs their applicability in high stake domains such as medicine. Providing insights into the survival distributions would tackle this issue and advance the medical understanding of diseases. This paper approaches survival analysis as a mixture of neural baselines whereby different baseline cumulative hazard functions are modelled using positive and monotone neural networks. The efficiency of the solution is demonstrated on three datasets while enabling the discovery of new survival phenotypes.