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Winning submission details vocal imitation sound querying strategies

By PulseAugur Editorial · [2 sources] ·

Researchers have detailed their winning submission for the AES AIMLA 2025 Challenge, which focused on querying sound effects through vocal imitation. Their approach involved two fine-tuning strategies: contrastive learning with a pre-trained encoder and joint contrastive-triplet learning with semi-hard negatives. The technical report includes details released after the challenge concluded. AI

IMPACT This research contributes to advancements in audio retrieval and AI-driven sound effect querying.

RANK_REASON The cluster contains an academic paper detailing a technical report on a research submission.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Winning submission details vocal imitation sound querying strategies

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang, Emmanouil Benetos ·

    Finetuning Strategies for Querying Sounds by Vocal Imitation

    arXiv:2608.19174v1 Announce Type: cross 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, pret…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Emmanouil Benetos ·

    Finetuning Strategies for Querying Sounds by Vocal Imitation

    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 …

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