  Nuclear Magnetic Resonance (NMR) is used in structural biology to
experimentally determine the structure of proteins, which is used in many areas
of biology and is an important part of drug development. Unfortunately, NMR
data can cost thousands of dollars per sample to collect and it can take a
specialist weeks to assign the observed resonances to specific chemical groups.
There has thus been growing interest in the NMR community to use deep learning
to automate NMR data annotation. Due to similarities between NMR and audio
data, we propose that methods used in acoustic signal processing can be applied
to NMR as well. Using a simulated amino acid dataset, we show that by swapping
out filter banks with a trainable convolutional encoder, acoustic signal
embeddings from speaker verification models can be used for amino acid
classification in 2D NMR spectra by treating each amino acid as a unique
speaker. On an NMR dataset comparable in size with of 46 hours of audio, we
achieve a classification performance of 97.7% on a 20-class problem. We also
achieve a 23% relative improvement by using an acoustic embedding model
compared to an existing NMR-based model.
