{"ID":2884812,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06405","arxiv_id":"2508.06405","title":"Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models","abstract":"Objective non-stationarity measures are resource intensive and impose critical limitations for real-time processing solutions. In this paper, a novel Hard Label Criteria (HLC) algorithm is proposed to generate a global non-stationarity label for acoustic signals, enabling supervised learning strategies to be trained as stationarity estimators. The HLC is first evaluated on state-of-the-art general-purpose acoustic models, demonstrating that these models capture stationarity information. Furthermore, the first-of-its-kind HLC-based Network for Acoustic Non-Stationarity Assessment (NANSA) is proposed. NANSA models outperform competing approaches, achieving up to 99% classification accuracy, while solving the computational infeasibility of traditional objective measures.","short_abstract":"Objective non-stationarity measures are resource intensive and impose critical limitations for real-time processing solutions. In this paper, a novel Hard Label Criteria (HLC) algorithm is proposed to generate a global non-stationarity label for acoustic signals, enabling supervised learning strategies to be trained as...","url_abs":"https://arxiv.org/abs/2508.06405","url_pdf":"https://arxiv.org/pdf/2508.06405v2","authors":"[\"Guilherme Zucatelli\",\"Ricardo Barioni\",\"Gabriela Dantas\"]","published":"2025-08-08T15:46:21Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"eess.SP\"]","methods":"[]","has_code":false}