---
res:
  bibo_abstract:
  - Running state-of-the-art large-scale audio models on edge devices is often infeasible
    due to their limited storage and computing resources. It is therefore necessary
    to compress and tune the models for the specific target task and hardware. This
    is commonly achieved by distilling the audio model, the teacher, to a small target
    model, the student. However, this approach can be improved by prepending a dataset
    pruning stage and training the teacher on the pruned data set only, which contains
    examples relevant to the target task. Recently, CLAP models have emerged that
    embed audio and text examples in a common embedding space. We use the audio embeddings
    of the CLAP model for the above pruning stage, which is realized using a domain
    classifier. After knowledge distillation, the student is eventually fine-tuned
    on some data from the target domain. The CLAP architecture combines text and audio
    embedding spaces, which allows to search for data given only a textual description,
    such as a class label. We show how this can help data pruning.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Werning, Alexander
      foaf_surname: Werning
      foaf_workInfoHomepage: http://www.librecat.org/personId=62152
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Häb-Umbach, Reinhold
      foaf_surname: Häb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  dct_date: 2025^xs_gYear
  dct_language: eng
  dct_title: Distilling Efficient Audio Models using Data Pruning with CLAP@
...