Finetuning Strategies for Querying Sounds by Vocal Imitation

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.

Sound (cs.SD); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2608.19174 [cs.SD]

(or arXiv:2608.19174v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2608.19174 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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