SeungHeon Doh
@SeungHeon_Doh
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LLM + Music | Postdoctoral researcher @ KAIST | Intern @SonyAI_global | Previously an intern @Adobe, @BytedanceTalk, @Naver, @Chartmetric.
Joined June 2022
Can a single framework (semantic IDs or embedding similarity) fully satisfy user needs in conversational music recommendation? As a solution, we present TalkPlay-Tools. - Demo: https://t.co/4EBs8bhDBp - Paper: https://t.co/GXiYlLk5bS - Code: https://t.co/vFW0CnBRsi
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NLP4MUSA 2026 officially announced a new challenge on conversational music recommendation. this year's challenge is based on TalkPlayData-2! i'm so honored to have contributed to this timely challenge.
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https://t.co/yheSQPWVQw @ RIT
tomorrow (10/23 Thu), 4:30PM EST. my talk on TalkPlay representing https://t.co/CTRcCcwzvW. at RIT (Rochester) / on Zoom https://t.co/9vr6coi9Lw
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This year: 1) NLP4MusA is expanding ISMIR to EACL 2) the Conversational Music Recommendation Challenge has been newly established! - Challenge Dataset: https://t.co/5eYpbEDKeJ - Baseline Code: https://t.co/Yv1ji0FERn - Evaluation Code:
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Call for Papers: NLP4MusA 2026 We welcome work at the intersection of language, music, and audio — from music understanding and recommendation to creative generation. Submission deadline: Dec 19, 2025 Details & submission:
sites.google.com
🎵 First Call for Papers: 4th Workshop on NLP for Music and Audio (NLP4MusA 2026) Co-located with EACL 2026, Rabat, Morocco & Online | March 24–29, 2026 Shared Task: Conversational Music Recommenda...
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New paper on symbolic music generation: first envisioning a theme, then composing the segments one-by-one in a flexible order. demo - https://t.co/YC0YuHcwMc code -
github.com
NeurIPS 2025 AI4Music Workshop: Segment-Factorized Full-Song Generation on Symbolic Piano Music - eri24816/segmented-full-song-gen
Ping-Yi Chen, Chih-Pin Tan, Yi-Hsuan Yang, "Segment-Factorized Full-Song Generation on Symbolic Piano Music,"
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TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling.
arxiv.org
While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is limited,...
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Seungheon Doh, Keunwoo Choi, Juhan Nam, "TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling,"
arxiv.org
While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is limited,...
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TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling @SeungHeon_Doh et al. position an LLM as an end-to-end recommendation system that interprets user intent and plans tool invocations for music discovery. 📝 https://t.co/fjp8PYMFUF 👨🏽💻 https://t.co/rCdjfes0yt
arxiv.org
While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is limited,...
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This work was carried out together with co-first author @keunwoochoi and @juhan_nam. We plan to present it at the NeurIPS AI4Music Workshop!
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Through a simple conversational music recommendation experiment, we confirm that the proposed tool calling framework outperforms zero-shot LLM recommendation models. (When both use Chain-of-thought)
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Tool calling doesn’t rely on a single search model; instead, it uses multiple search models sequentially, supporting a retrieval–reranking framework.
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We started this project with the idea that different user queries require different models: numerical values like tempo or popularity suit SQL, artist or album searches work best with BM25, personalization fits CF models, and timbre-based searches rely on audio embeddings.
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TalkPlay-Tools consists of Music Recommendation Agents and an external environment. We use various music search and personalization models as tools.
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I am officially on the job market!! Looking for industry and faculty/postdoc starting Fall 2026, especially in Audio/Music Gen and Audio-LLMs 🥳 I'll be at #ISMIR2025 this week (super pumped!), where I'll present 3 papers in AI Music eval and datasets if you want to chat! 🇰🇷
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A New Conversational Music Recommendation Dataset with (1) User Profile, (2) Conversation Goal, (3) Chain of Thought, and (4) Multimodal Item Representation. https://t.co/iONg8vZUEH
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Great audio AEs/codecs exist, but when you need structured latents or a tweaked bottleneck for a downstream task (e.g. generation), retraining is expensive & brittle. We Re-Bottleneck👇
Dimitrios Bralios, Jonah Casebeer, Paris Smaragdis, "Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders,"
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New to ISMIR? Join the Newcomer Squad! Have you participated in ISMIR multiple times? Become a Newcomer Leader!
Join the ISMIR2025 Newcomer Squad – Connect, Learn & Enjoy! 📷 Is this your first ISMIR and you’re not sure how to make the most of it? Or are you a seasoned attendee who’d love to give back and welcome newcomers? Either way, the Newcomer Squad at ISMIR2025 (South Korea!) is
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Realtime interactive generative models FTW! Announcing a new 🌊 of details and features for Magenta RealTime, the open weights live music AI model from GDM! * Live Jamming with audio input 🎤🎸🎵 * Personalize your own models 🔧 * Tech report 📜 Links below in the 🧵...
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Suno + Veo 3 generate highly similar versions of popular songs purely based on *phonetically* similar gibberish lyrics?!?! Presenting Bob’s Confetti: Phonetic Memorization Attacks in Music and Video Generation 🔊: https://t.co/Rztf9TNhI7 📖: https://t.co/H7BgnUkCvD 🧵1/n
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