---
_id: '57160'
abstract:
- lang: eng
  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.
author:
- first_name: Alexander
  full_name: Werning, Alexander
  id: '62152'
  last_name: Werning
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  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.'
  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.
  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} }'
  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.
  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.
  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.
  short: 'A. Werning, R. Haeb-Umbach, in: 32nd European Signal Processing Conference
    (EUSIPCO 2024), 2024.'
conference:
  location: Lyon
  name: 32nd European Signal Processing Conference
date_created: 2024-11-18T09:29:16Z
date_updated: 2025-11-28T13:22:00Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: closed
  content_type: application/pdf
  creator: awerning
  date_created: 2024-11-18T12:10:09Z
  date_updated: 2024-11-18T12:10:09Z
  file_id: '57200'
  file_name: Eusipco__Target_specific_Dataset_Pruning_for_Compression_of_Audio_Tagging_Models.pdf
  file_size: 183539
  relation: main_file
  success: 1
file_date_updated: 2024-11-18T12:10:09Z
has_accepted_license: '1'
keyword:
- data pruning
- knowledge distillation
- audio tagging
language:
- iso: eng
project:
- _id: '512'
  name: WestAI - AI Service Center West
publication: 32nd European Signal Processing Conference (EUSIPCO 2024)
quality_controlled: '1'
status: public
title: Target-Specific Dataset Pruning for Compression of Audio Tagging Models
type: conference
user_id: '62152'
year: '2024'
...