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

arXiv 2311.12130 ↗First posted 16 Nov 2023cs.SD cs.LG eess.AScited by 0

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Abstract (arXiv)

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.

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References · 20

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8 more
  1. 2020Verifying Recurrent Neural Networks using Invariant Inferencecited by 26
  2. 2018Formal Verification of CNN-based Perception Systemscited by 13
  3. 2018Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classificationcited by 7
  4. 2022A Tool for Neural Network Global Robustness Certification and Trainingcited by 5
  5. 2018Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the L0 Normcited by 4
  6. 2023A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systemscited by 3
  7. 2023Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Inputcited by 2
  8. 2022Improving the Environmental Perception of Autonomous Vehicles using Deep Learning-based Audio Classificationcited by 1

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