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arXiv · 2608.19174

Finetuning Strategies for Querying Sounds by Vocal Imitation

Aditya Bhattacharjee·Christos Plachouras·Sungkyun Chang·Emmanouil Benetos
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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.

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Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang, Emmanouil Benetos. 2026-08-19. Finetuning Strategies for Querying Sounds by Vocal Imitation. https://arxiv.org/abs/2608.19174

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