  Musical Instrument Identification has for long had a reputation of being one
of the most ill-posed problems in the field of Musical Information
Retrieval(MIR). Despite several robust attempts to solve the problem, a
timeline spanning over the last five odd decades, the problem remains an open
conundrum. In this work, the authors take on a further complex version of the
traditional problem statement. They attempt to solve the problem with minimal
data available - one audio excerpt per class. We propose to use a convolutional
Siamese network and a residual variant of the same to identify musical
instruments based on the corresponding scalograms of their audio excerpts. Our
experiments and corresponding results obtained on two publicly available
datasets validate the superiority of our algorithm by $\approx$ 3\% over the
existing synonymous algorithms in present-day literature.
