  In this work, we explore recurrent neural network architectures for
tuberculosis (TB) cough classification. In contrast to previous unsuccessful
attempts to implement deep architectures in this domain, we show that a basic
bidirectional long short-term memory network (BiLSTM) can achieve improved
performance. In addition, we show that by performing greedy feature selection
in conjunction with a newly-proposed attention-based architecture that learns
patient invariant features, substantially better generalisation can be achieved
compared to a baseline and other considered architectures. Furthermore, this
attention mechanism allows an inspection of the temporal regions of the audio
signal considered to be important for classification to be performed. Finally,
we develop a neural style transfer technique to infer idealised inputs which
can subsequently be analysed. We find distinct differences between the
idealised power spectra of TB and non-TB coughs, which provide clues about the
origin of the features in the audio signal.
