  Supervised learning is a mainstream approach to audio signal enhancement (SE)
and requires parallel training data consisting of both noisy signals and the
corresponding clean signals. Such data can only be synthesised and are
mismatched with real data, which can result in poor performance on real data.
Moreover, clean signals may be inaccessible in certain scenarios, which renders
this conventional approach infeasible. Here we explore SE using non-parallel
training data consisting of noisy signals and noise, which can be easily
recorded. We define the positive (P) and the negative (N) classes as signal
inactivity and activity, respectively. We observe that the spectrogram patches
of noise clips can be used as P data and those of noisy signal clips as
unlabelled data. Thus, learning from positive and unlabelled data enables a
convolutional neural network to learn to classify each spectrogram patch as P
or N to enable SE.
