Dmitrii Mukhutdinov

Speech enhancement in the presence of strong drone noise at extremely low signal-to-noise ratios (SNRs) remains challenging, and conventional objective metrics may fail to accurately reflect perceived speech quality. In this paper, we evaluate several deep learning-based speech enhancement algorithms under severe drone-noise conditions using a diverse set of objective metrics, including signal fidelity, intelligibility, and perceptual quality measures. The results reveal substantial inconsistencies among metrics, leading to conflicting conclusions regarding algorithm performance. To address this issue, we conduct a controlled subjective listening experiment under extremely low-SNR conditions to assess the perceptual relevance of these metrics. The consistency analysis shows that learning-based perceptual metrics (e.g., NISQA) and intelligibility-oriented measures (e.g., ESTOI) achieve the highest agreement with subjective judgments, followed by SI-SDR, while traditional quality metrics such as PESQ exhibit weaker agreement. These findings highlight the limitations of conventional metrics in extremely low-SNR conditions and provide guidance for selecting perceptually meaningful evaluation criteria in drone audition.