  Acoustic monitoring has recently shown great potential in the diagnosis of
infrastructure condition. However, due to the severe noise interference in
acoustic signals, meaningful features tend to be difficult to infer. It creates
a considerable obstacle for an extensive application of acoustic monitoring. To
tackle this problem, we propose an acceleration-guided acoustic signal
denoising framework (AG-ASDF) based on learnable wavelet transform to
automatically denoise the acoustic signal and extract the relevant features
based on the acceleration signal. This denoising framework requires the
acceleration signal only for the training stage. Therefore, only acoustic
sensors (non-intrusive) need to be installed during the application phase,
which is convenient and crucial for the condition monitoring of safety-critical
infrastructure. A comparative study is conducted among the proposed AG-ASDF and
other feature learning / extraction methods, by using a multi-class support
vector machine to evaluate the detection effectiveness of slab track condition
based on acoustic signals. Different healthy and unhealthy states of slab
tracks are imitated with three types of slab track supporting conditions in a
railway test line. The classification based on the proposed AG-ASDF features
outperforms other feature extraction and learning methods with a significant
accuracy improvement.
