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Finetuning Strategies for Querying Sounds by Vocal Imitation

arXiv 2608.19174 ↗First posted 19 Aug 2026cs.SD cs.AI cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 19 Aug 2026.

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References · 2

  1. 2023CED: Consistent ensemble distillation for audio taggingcited by 29
  2. 2024Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretrainingcited by 2

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