  The scattering framework offers an optimal hierarchical convolutional
decomposition according to its kernels. Convolutional Neural Net (CNN) can be
seen as an optimal kernel decomposition, nevertheless it requires large amount
of training data to learn its kernels. We propose a trade-off between these two
approaches: a Chirplet kernel as an efficient Q constant bioacoustic
representation to pretrain CNN. First we motivate Chirplet bioinspired auditory
representation. Second we give the first algorithm (and code) of a Fast
Chirplet Transform (FCT). Third, we demonstrate the computation efficiency of
FCT on large environmental data base: months of Orca recordings, and 1000 Birds
species from the LifeClef challenge. Fourth, we validate FCT on the vowels
subset of the Speech TIMIT dataset. The results show that FCT accelerates CNN
when it pretrains low level layers: it reduces training duration by -28\% for
birds classification, and by -26% for vowels classification. Scores are also
enhanced by FCT pretraining, with a relative gain of +7.8% of Mean Average
Precision on birds, and +2.3\% of vowel accuracy against raw audio CNN. We
conclude on perspectives on tonotopic FCT deep machine listening, and
inter-species bioacoustic transfer learning to generalise the representation of
animal communication systems.
