[{"quality_controlled":"1","has_accepted_license":"1","citation":{"ieee":"A. Werning and R. Haeb-Umbach, “Target-Specific Dataset Pruning for Compression of Audio Tagging Models,” presented at the 32nd European Signal Processing Conference, Lyon, 2024.","chicago":"Werning, Alexander, and Reinhold Haeb-Umbach. “Target-Specific Dataset Pruning for Compression of Audio Tagging Models.” In <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>, 2024.","ama":"Werning A, Haeb-Umbach R. Target-Specific Dataset Pruning for Compression of Audio Tagging Models. In: <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>. ; 2024.","short":"A. Werning, R. Haeb-Umbach, in: 32nd European Signal Processing Conference (EUSIPCO 2024), 2024.","mla":"Werning, Alexander, and Reinhold Haeb-Umbach. “Target-Specific Dataset Pruning for Compression of Audio Tagging Models.” <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>, 2024.","bibtex":"@inproceedings{Werning_Haeb-Umbach_2024, title={Target-Specific Dataset Pruning for Compression of Audio Tagging Models}, booktitle={32nd European Signal Processing Conference (EUSIPCO 2024)}, author={Werning, Alexander and Haeb-Umbach, Reinhold}, year={2024} }","apa":"Werning, A., &#38; Haeb-Umbach, R. (2024). Target-Specific Dataset Pruning for Compression of Audio Tagging Models. <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>. 32nd European Signal Processing Conference, Lyon."},"year":"2024","date_created":"2024-11-18T09:29:16Z","author":[{"full_name":"Werning, Alexander","id":"62152","last_name":"Werning","first_name":"Alexander"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold"}],"date_updated":"2025-11-28T13:22:00Z","conference":{"location":"Lyon","name":"32nd European Signal Processing Conference"},"title":"Target-Specific Dataset Pruning for Compression of Audio Tagging Models","type":"conference","publication":"32nd European Signal Processing Conference (EUSIPCO 2024)","file":[{"file_size":183539,"file_id":"57200","access_level":"closed","file_name":"Eusipco__Target_specific_Dataset_Pruning_for_Compression_of_Audio_Tagging_Models.pdf","date_updated":"2024-11-18T12:10:09Z","date_created":"2024-11-18T12:10:09Z","creator":"awerning","success":1,"relation":"main_file","content_type":"application/pdf"}],"status":"public","abstract":[{"text":"Large audio tagging models are usually trained or pre-trained on AudioSet, a dataset that encompasses a large amount of different sound classes and acoustic environments. Knowledge distillation has emerged as a method to compress such models without compromising their effectiveness. There are many different applications for audio tagging, some of which require a specialization to a narrow domain of sounds to be classified. For these scenarios, it is beneficial to distill the large audio tagger with respect to a specific subset of sounds of interest. A method to prune a general dataset with respect to a target dataset is presented. By distilling with such a specialized pruned dataset, we obtain a compressed model with better classification accuracy in the specific target domain than with target-agnostic distillation.","lang":"eng"}],"user_id":"62152","department":[{"_id":"54"}],"project":[{"name":"WestAI - AI Service Center West","_id":"512"}],"_id":"57160","file_date_updated":"2024-11-18T12:10:09Z","language":[{"iso":"eng"}],"ddc":["000"],"keyword":["data pruning","knowledge distillation","audio tagging"]}]