Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915743924224000 |
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| author | Zucatelli, Guilherme Barioni, Ricardo Dantas, Gabriela |
| author_facet | Zucatelli, Guilherme Barioni, Ricardo Dantas, Gabriela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06405 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models Zucatelli, Guilherme Barioni, Ricardo Dantas, Gabriela Audio and Speech Processing Signal Processing 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. |
| title | Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models |
| topic | Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2508.06405 |