Model and Algorithms for Classifying Anomalous Phenomena based on the Convergence of Acoustic-Visual Signals

Ravshanov, N., Boborakhimov, B.I., Berdiev, M.I., Равшанов, Н., Боборахимов, Б.И., Бердиев, М.И.

Ҳисоблаш ва амалий математика муаммолари · 2026-yil

Annotatsiya

This paper proposes a Context-adaptive Audio-Visual Neural Network (CAVN) model for anomaly detection in public safety systems. Existing approaches primarily rely on visual data and employ simple fusion strategies for combining modalities, which leads to limitations in capturing complex semantic relationships. The proposed model consists of four main components: a visual feature extraction module based on SlowFast architecture, an audio feature extraction module based on Audio Spectrogram Transformer (AST), a fusion module based on bidirectional cross-attention mechanism, and a temporal context aggregation module based on Transformer encoder. The main scientific novelty of the model lies in the adaptive modality balancing mechanism, which dynamically adjusts the relative importance of modalities under different conditions (dark/bright, noisy/quiet). Experimental results demonstrate that the proposed CAVN model outperforms existing methods by in overall accuracy and by in dark conditions. Ablation studies confirmed the contribution of each module to the overall performance.

Maqola ma’lumotlari
MualliflarRavshanov, N., Boborakhimov, B.I., Berdiev, M.I., Равшанов, Н., Боборахимов, Б.И., Бердиев, М.И.
JurnalҲисоблаш ва амалий математика муаммолари
NashriyotТошкент ахборот технологиялари университети қошидаги, Ахборот-коммуникация технологиялари илмий-инновацион, маркази
ISSN2181-8460, 2181-046X
Nashr sanasi2026-01-11
Son6
Betlar88-102
TilIngliz
Ilmiy sohaTexnika
DOI10.71310/pcam.6_70.2025.07
Indeksga qo‘shilgan2026-08-27

DOI: 10.71310/pcam.6_70.2025.07 · Maqolaning asl sahifasi

Kalit so‘zlar

dynamic weighting, spatiotemporal representation learning, attention-based alignment, robust anomaly recognition, real-world surveillance, динамическое взвешивание модальностей, пространственно-временное представление, выравнивание на основе внимания, устойчивое распознавание аномалий, видеонаблюдение в реальных условиях

Ilmiy soha

Texnika

Iqtibos keltirish

APARavshanov, N., Boborakhimov, B.I., Berdiev, M.I., Равшанов, Н., Боборахимов, Б.И., & Бердиев, М.И. (2026). Model and Algorithms for Classifying Anomalous Phenomena based on the Convergence of Acoustic-Visual Signals. Ҳисоблаш ва амалий математика муаммолари, (6), 88-102. https://doi.org/10.71310/pcam.6_70.2025.07
GOST (OAK)Ravshanov, N. va b. Model and Algorithms for Classifying Anomalous Phenomena based on the Convergence of Acoustic-Visual Signals // Ҳисоблаш ва амалий математика муаммолари. – 2026. – № 6. – P. 88-102. – DOI: 10.71310/pcam.6_70.2025.07.
MLARavshanov, N., Boborakhimov, B.I., Berdiev, M.I., Равшанов, Н., Боборахимов, Б.И., and Бердиев, М.И. “Model and Algorithms for Classifying Anomalous Phenomena based on the Convergence of Acoustic-Visual Signals.” Ҳисоблаш ва амалий математика муаммолари, (6), 88-102, 2026. https://doi.org/10.71310/pcam.6_70.2025.07
ChicagoRavshanov, N., Boborakhimov, B.I., Berdiev, M.I., Равшанов, Н., Боборахимов, Б.И., and Бердиев, М.И. “Model and Algorithms for Classifying Anomalous Phenomena based on the Convergence of Acoustic-Visual Signals.” Ҳисоблаш ва амалий математика муаммолари, (6), 88-102 (2026). https://doi.org/10.71310/pcam.6_70.2025.07
HarvardRavshanov, N., Boborakhimov, B.I., Berdiev, M.I., Равшанов, Н., Боборахимов, Б.И., and Бердиев, М.И (2026) ‘Model and Algorithms for Classifying Anomalous Phenomena based on the Convergence of Acoustic-Visual Signals’, Ҳисоблаш ва амалий математика муаммолари, (6), 88-102. https://doi.org/10.71310/pcam.6_70.2025.07
BibTeX
@article{ravshanovn2026,
  title = {Model and Algorithms for Classifying Anomalous Phenomena based on the Convergence of Acoustic-Visual Signals},
  author = {Ravshanov, N. and Boborakhimov, B.I. and Berdiev, M.I. and Равшанов, Н. and Боборахимов, Б.И. and Бердиев, М.И.},
  journal = {Ҳисоблаш ва амалий математика муаммолари},
  year = {2026},
  number = {6},
  pages = {88-102},
  doi = {10.71310/pcam.6_70.2025.07},
  url = {https://doi.org/10.71310/pcam.6_70.2025.07}
}

Boshqa maqola uchun iqtibos tayyorlash

Ҳисоблаш ва амалий математика муаммолари jurnalidan boshqa maqolalar

Ҳисоблаш ва амалий математика муаммолари — barcha maqolalar · 2026-yil arxivi

Texnika sohasidagi boshqa maqolalar

Texnika sohasidagi OAK jurnallari