Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach

Abstract

Formally verifying audio classification systems is essential to ensure accurate signal classification across real-world applications like surveillance, automotive voice commands, and multimedia content management, preventing potential errors with serious consequences. Drawing from recent research, this study advances the utilization of star-set-based formal verification, extended through reachability analysis, tailored explicitly for Long Short-Term Memory architectures and their Convolutional variations within the audio classification domain. By conceptualizing the classification process as a sequence of set operations, the star set-based reachability approach streamlines the exploration of potential operational states attainable by the system. The paper serves as an encompassing case study, validating and verifying sequence audio classification analytics within real-world contexts. It accentuates the necessity for robustness verification to ensure precise and dependable predictions, particularly in light of the impact of noise on the accuracy of output classifications.

In Proceedings FMAS 2023, arXiv:2311.08987

References in corpus (5)

• Explaining and Harnessing Adversarial Examples
• WaveNet: A Generative Model for Raw Audio
• Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
• A Tool for Neural Network Global Robustness Certification and Training
• Improving the Environmental Perception of Autonomous Vehicles using Deep Learning-based Audio Classification