30 citations · 52 across the 25 of their papers we have counts for
28 papers
Agentic Music Post-Production
Jaywon Koo, Junghyun Koo, Marco A. Martinez-Ramirez +5
We introduce MixAgent, an agentic pipeline for music post-production that follows the workflow of audio engineers to build interpretable and editable mixing plans guided by a refer…
Overview and Analysis of the RecSys Challenge 2026: Conversational Music Recommendation
Seungheon Doh, Sergio Oramas, Bruno Sguerra +3
The RecSys Challenge 2026 studies conversational music recommendation as a joint item recommendation and response generation problem: given a multi-turn dialogue, systems must retr…
LLM-as-a-Judge for Evaluating System Responses in Conversational Music Recommendation
Seungheon Doh, Bruno Sguerra, Sergio Oramas +2
Conversational Recommendation Systems (CRS) aim to achieve two primary objectives: recommending relevant items and generating natural language responses. While recommendation accur…
Multimodal Video-to-Music Recommendation via Semantic Retrieval and Temporal Reranking
Seungheon Doh, Minhee Lee, Sangmoon Lee +2
We present VTMR, a two-stage framework for Video-To-Music Recommendation. In Stage~1, VTMR aligns comprehensive video and music signals in a joint audio-visual-text representation…
LLM2Fx-Tools: Tool Calling For Music Post-Production
Seungheon Doh, Junghyun Koo, Marco A. Martínez-Ramírez +5
This paper introduces LLM2Fx-Tools, a multimodal tool-calling framework that generates executable sequences of audio effects (Fx-chain) for music post-production. LLM2Fx-Tools uses…
ArtistMus: A Globally Diverse, Artist-Centric Benchmark for Retrieval-Augmented Music Question Answering
Daeyong Kwon, SeungHeon Doh, Juhan Nam
Recent advances in large language models (LLMs) have transformed open-domain question answering, yet their effectiveness in music-related reasoning remains limited due to sparse mu…
TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling
Seungheon Doh, Keunwoo Choi, Juhan Nam
While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is…
PianoBind: A Multimodal Joint Embedding Model for Pop-piano Music
Hayeon Bang, Eunjin Choi, Seungheon Doh +1
Solo piano music, despite being a single-instrument medium, possesses significant expressive capabilities, conveying rich semantic information across genres, moods, and styles. How…
TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
Keunwoo Choi, Seungheon Doh, Juhan Nam
We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline. In the proposed pipeline, multiple large la…
MUST-RAG: MUSical Text Question Answering with Retrieval Augmented Generation
Daeyong Kwon, SeungHeon Doh, Juhan Nam
Recent advancements in Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains. While they exhibit strong zero-shot performance on various tas…
Can Large Language Models Predict Audio Effects Parameters from Natural Language?
Seungheon Doh, Junghyun Koo, Marco A. Martínez-Ramírez +3
In music production, manipulating audio effects (Fx) parameters through natural language has the potential to reduce technical barriers for non-experts. We present LLM2Fx, a framew…
TALKPLAY: Multimodal Music Recommendation with Large Language Models
Seungheon Doh, Keunwoo Choi, Juhan Nam
We present TALKPLAY, a novel multimodal music recommendation system that reformulates recommendation as a token generation problem using large language models (LLMs). By leveraging…
CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages
Shangda Wu, Zhancheng Guo, Ruibin Yuan +7
CLaMP 3 is a unified framework developed to address challenges of cross-modal and cross-lingual generalization in music information retrieval. Using contrastive learning, it aligns…
Predicting User Intents and Musical Attributes from Music Discovery Conversations
Daeyong Kwon, SeungHeon Doh, Juhan Nam
Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains,…
Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models
SeungHeon Doh, Keunwoo Choi, Daeyong Kwon +2
A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system shou…
PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
Hayeon Bang, Eunjin Choi, Megan Finch +4
While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address…
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music Retrieval
SeungHeon Doh, Minhee Lee, Dasaem Jeong +1
Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challeng…
Musical Word Embedding for Music Tagging and Retrieval
SeungHeon Doh, Jongpil Lee, Dasaem Jeong +1
Word embedding has become an essential means for text-based information retrieval. Typically, word embeddings are learned from large quantities of general and unstructured text dat…
The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation
Ilaria Manco, Benno Weck, SeungHeon Doh +10
We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models. The dataset…
LP-MusicCaps: LLM-Based Pseudo Music Captioning
SeungHeon Doh, Keunwoo Choi, Jongpil Lee +1
Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of la…
Textless Speech-to-Music Retrieval Using Emotion Similarity
SeungHeon Doh, Minz Won, Keunwoo Choi +1
We introduce a framework that recommends music based on the emotions of speech. In content creation and daily life, speech contains information about human emotions, which can be e…
Music Playlist Title Generation Using Artist Information
Haven Kim, SeungHeon Doh, Junwon Lee +1
Automatically generating or captioning music playlist titles given a set of tracks is of significant interest in music streaming services as customized playlists are widely used in…
Toward Universal Text-to-Music Retrieval
SeungHeon Doh, Minz Won, Keunwoo Choi +1
This paper introduces effective design choices for text-to-music retrieval systems. An ideal text-based retrieval system would support various input queries such as pre-defined tag…
Hi,KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
Taesu Kim, SeungHeon Doh, Gyunpyo Lee +3
Wake-up words (WUW) is a short sentence used to activate a speech recognition system to receive the user's speech input. WUW utterances include not only the lexical information for…