Finetuning Strategies for Querying Sounds by Vocal Imitation

Computer Science > Sound arXiv:2608.19174v1 (cs) [Submitted on 19 Aug 2026] Title:Finetuning Strategies for Querying Sounds by Vocal Imitation Authors:Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang, Emmanouil Benetos View a PDF of the paper titled Finetuning Strategies for Querying Sounds by Vocal Imitation, by Aditya Bhattacharjee and 3 other authors View PDF HTML (experimental) 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. Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) ACM classes: H.5.5; I.2.6 Cite as: 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) Submission history From: Aditya Bhattacharjee [view email] [v1] Wed, 19 Aug 2026 17:51:50 UTC (6 KB) Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)