Finetuning Strategies for Querying Sounds by Vocal Imitation
Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang, Emmanouil Benetos
cs.SD cs.AI cs.IR
Abstract
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.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.