Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Presannakumar, Krishna; Mohamed, Anuj Title A name given to the resource Enhancing environmental sound classification with weighted attention-based spectrogram fusion and overlapping pre-patching Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Applied Soft Computing;Volume;186;Issue;;Article No.;114192; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.asoc.2025.114192" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asoc.2025.114192</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021486523?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021486523?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Presannakumar K., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Mohamed A., School of Computer Sciences, Mahatma Gandhi University, Kerala, Kottayam, 686560, India Description An account of the resource Environmental Sound Classification (ESC) remains challenging due to the diverse and overlapping acoustic characteristics of real-world environments. Traditional models relying on single-feature representations such as Mel spectrograms often fail to capture the full range of spectral and temporal details. This paper introduces a novel algorithm Weighted Attention-based Spectrogram Fusion (WASF) that adaptively integrates Mel spectrograms, Cochleograms, and Correlograms using a hierarchical attention mechanism across channel, temporal, and frequency dimensions. Compared to traditional fusion techniques, WASF uses a learnable attention mechanism to dynamically weight each feature's importance over time and frequency, improving the model's capacity to focus on important acoustic cues. In addition, an overlapping pre-patching strategy is proposed to preserve local temporal continuity, enhancing transformer-based modeling. Proposed model demonstrates superior performance with 95.71 % accuracy on UrbanSound8K, 93.97 % on ESC-50, and 94.91 % on ESC-10 datasets. Extensive ablation studies and interpretability analysis validate the effectiveness of each component, demonstrating robustness across diverse acoustic environments and noise conditions. The computational efficiency and interpretable attention patterns make our approach suitable for real-time deployment in smart city applications, surveillance systems, and assistive technologies. 2025 Elsevier B.V. Subject The topic of the resource Audio sound signal; Deep learning; Environmental sound classification; Feature representation; Signal processing; Transformers Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 15684946; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online