{"ID":2843049,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09802","arxiv_id":"2511.09802","title":"Investigation of Feature Selection and Pooling Methods for Environmental Sound Classification","abstract":"This paper explores the impact of dimensionality reduction and pooling methods for Environmental Sound Classification (ESC) using lightweight CNNs. We evaluate Sparse Salient Region Pooling (SSRP) and its variants, SSRP-Basic (SSRP-B) and SSRP-Top-K (SSRP-T), under various hyperparameter settings and compare them with Principal Component Analysis (PCA). Experiments on the ESC-50 dataset demonstrate that SSRP-T achieves up to 80.69 % accuracy, significantly outperforming both the baseline CNN (66.75 %) and the PCA-reduced model (37.60 %). Our findings confirm that a well-tuned sparse pooling strategy provides a robust, efficient, and high-performing solution for ESC tasks, particularly in resource-constrained scenarios where balancing accuracy and computational cost is crucial.","short_abstract":"This paper explores the impact of dimensionality reduction and pooling methods for Environmental Sound Classification (ESC) using lightweight CNNs. We evaluate Sparse Salient Region Pooling (SSRP) and its variants, SSRP-Basic (SSRP-B) and SSRP-Top-K (SSRP-T), under various hyperparameter settings and compare them with...","url_abs":"https://arxiv.org/abs/2511.09802","url_pdf":"https://arxiv.org/pdf/2511.09802v1","authors":"[\"Parinaz Binandeh Dehaghani\",\"Danilo Pena\",\"A. Pedro Aguiar\"]","published":"2025-11-12T23:07:28Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.SD\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}