  This paper presents an approach for instantaneous bandwidth estimation from
level-crossing samples using a long short-term memory (LSTM) encoder-decoder
architecture. Level-crossing sampling is a nonuniform sampling technique that
is particularly useful for energy-efficient acquisition of signals with sparse
spectra. Especially in combination with fully analog wireless sensor nodes,
level-crossing sampling offers a viable alternative to traditional sampling
methods. However, due to the nonuniform distribution of samples, reconstructing
the original signal is a challenging task. One promising reconstruction
approach is time-warping, where the local signal spectrum is taken into
account. However, this requires an accurate estimate of the instantaneous
bandwidth of the signal. In this paper, we show that applying neural networks
(NNs) to the problem of estimating instantaneous bandwidth from level-crossing
samples can improve the overall reconstruction accuracy. We conduct a
comprehensive numerical analysis of the proposed approach and compare it to an
intensity-based bandwidth estimation method from literature.
