Finetuning Strategies for Querying Sounds by Vocal Imitation
This technical report describes our winning submission to the AES AIMLA 2025 Challenge on querying sound effects by vocal imitation. We investigate two complementary fine-tuning strategies: contrastive learning with a frozen, pretrained CED encoder, and joint contrastive-triplet learning with semi-hard negatives using a MobileNetV3 encoder. This report has been updated for posterity to include details released after the challenge.
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- LinkedLinked via arxiv author · 85%Aditya Bhattacharjee →
“Finetuning Strategies for Querying Sounds by Vocal Imitation”
- LinkedLinked via arxiv author · 85%Christos Plachouras →
“Finetuning Strategies for Querying Sounds by Vocal Imitation”
- LinkedLinked via arxiv author · 85%Sungkyun Chang →
“Finetuning Strategies for Querying Sounds by Vocal Imitation”