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.

通过使用基于星集的形式验证方法验证音频分类系统，本研究旨在简化长短时记忆结构和卷积变体在音频分类领域中的可及性分析，从而在实际应用中确保准确的信号分类，强调了在噪声对输出分类准确性产生影响的情况下，鲁棒性验证对于确保精确可靠的预测的必要性。