Audio Classification of Low Feature Spectrograms Utilizing Convolutional Neural Networks
This addresses the challenge of audio signal classification for real-world applications where data is limited or skewed, representing an incremental advance in domain-specific techniques.
The paper tackled the problem of classifying low-feature audio spectrograms with non-representative training data by proposing novel customized convolutional architectures and classification methods, achieving state-of-the-art accuracy and improved efficiency.
Modern day audio signal classification techniques lack the ability to classify low feature audio signals in the form of spectrographic temporal frequency data representations. Additionally, currently utilized techniques rely on full diverse data sets that are often not representative of real-world distributions. This paper derives several first-of-its-kind machine learning methodologies to analyze these low feature audio spectrograms given data distributions that may have normalized, skewed, or even limited training sets. In particular, this paper proposes several novel customized convolutional architectures to extract identifying features using binary, one-class, and siamese approaches to identify the spectrographic signature of a given audio signal. Utilizing these novel convolutional architectures as well as the proposed classification methods, these experiments demonstrate state-of-the-art classification accuracy and improved efficiency than traditional audio classification methods.