7 citations · 12 across the 19 of their papers we have counts for
20 papers
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…
Motive-level Analysis of Form-functions Association in Korean Folk song
Danbinaerin Han, Dasaem Jeong, Juhan Nam
Computational analysis of folk song audio is challenging due to structural irregularities and the need for manual annotation. We propose a method for automatic motive segmentation…
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…
Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models
Taegyun Kwon, Dasaem Jeong, Juhan Nam
In recent years, advancements in neural network designs and the availability of large-scale labeled datasets have led to significant improvements in the accuracy of piano transcrip…
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…
K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling
Haven Kim, Jongmin Jung, Dasaem Jeong +1
Lyric translation, a field studied for over a century, is now attracting computational linguistics researchers. We identified two limitations in previous studies. Firstly, lyric tr…