  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.

Sound;Machine Learning;Audio and Speech Processing