arXiv AI August 20, 2026 By Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang, Emmanouil Benetos

Finetuning Strategies for Querying Sounds by Vocal Imitation

The report details the winning approach for the AES AIMLA 2025 Challenge on querying sound effects by vocal imitation. It explores two fine‑tuning strategies: contrastive learning with a frozen, pretrained CED encoder, and joint contrastive‑triplet learning using a MobileNetV3 encoder with semi‑hard negatives. Updated post‑challenge, the document includes additional released details.

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