2026-04-21T12:51:01Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00234740 2025-01-19T09:42:27Z 1164:5159:11541:11627

Enhancing Feature Integration to Improve Classification Accuracy of Similar Categories in Acoustic Scene Classification Enhancing Feature Integration to Improve Classification Accuracy of Similar Categories in Acoustic Scene Classification Shuting, Hao Daisuke, Saito Nobuaki, Minematsu Shuting, Hao Daisuke, Saito Nobuaki, Minematsu ポスターセッション2 This study focuses on Acoustic Scene Classification (ASC), which categorizes environmental audio streams into predefined semantic labels. We introduce a novel architecture that integrates multi-layer classifiers and direct finetuning, presenting a new perspective in ASC research. The study employs the TAU Urban Acoustic Scenes 2022 Mobile dataset for fine-tuning and validation. We utilized the SSAST model, pre-trained on the AudioSet and LibriSpeech datasets, and fine-tuned it on the TAU dataset with a unique approach to enhance ASC-specific feature learning. Our layered SSAST system achieved an accuracy of 52.17% and an AUC of 88.66% in ASC, marking a notable improvement over the baseline with absolute increases of 0.99% in accuracy and 0.85% in AUC. This study focuses on Acoustic Scene Classification (ASC), which categorizes environmental audio streams into predefined semantic labels. We introduce a novel architecture that integrates multi-layer classifiers and direct finetuning, presenting a new perspective in ASC research. The study employs the TAU Urban Acoustic Scenes 2022 Mobile dataset for fine-tuning and validation. We utilized the SSAST model, pre-trained on the AudioSet and LibriSpeech datasets, and fine-tuned it on the TAU dataset with a unique approach to enhance ASC-specific feature learning. Our layered SSAST system achieved an accuracy of 52.17% and an AUC of 88.66% in ASC, marking a notable improvement over the baseline with absolute increases of 0.99% in accuracy and 0.85% in AUC. technical report 情報処理学会 2024-06-07 application/pdf 研究報告音声言語情報処理（SLP） 53 2024-SLP-152 1 5 2188-8663 AN10442647 https://ipsj.ixsq.nii.ac.jp/record/234740/files/IPSJ-SLP24152053.pdf eng