Deep Single Shot Musical Instrument Identification using Scalograms

TL;DR

This paper introduces a deep learning approach using Siamese networks and scalograms to identify musical instruments from minimal data, achieving improved accuracy over existing methods.

Contribution

The authors propose a novel deep learning framework with Siamese networks and residual connections for instrument identification using only one audio excerpt per class.

Findings

Achieved approximately 3% higher accuracy than existing algorithms.

Validated on two public datasets, demonstrating robustness.

Effective with minimal data per class.

Abstract

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.