Keunwoo Choi — accepted AI-conference papers

2026

TwinWeaver: An LLM-Based Foundation Model Framework for Pan-Cancer Digital Twins

Nikita Makarov, Maria Bordukova, Lena von Voithenberg, Estrella Pivel-Villanueva, Sabrina Mielke, Jonathan Wickes +8

ICML 2026poster

Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction …

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2023

Medleyvox: An Evaluation Dataset for Multiple Singing Voices Separation

Chang-Bin Jeon, Hyeongi Moon, Keunwoo Choi, Ben Sangbae Chon, Kyogu Lee

ICASSP 2023accepted

Separation of multiple singing voices into each voice is a rarely studied area in music source separation research. The absence of a benchmark dataset has hindered its progress. In this paper, we present an evaluation dataset and provide baseline studies for multiple singing voices separation. First…

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2023

Textless Speech-to-Music Retrieval Using Emotion Similarity

Seungheon Doh, Minz Won, Keunwoo Choi, Juhan Nam

ICASSP 2023accepted

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 enhanced by music. Our framework focuses on a cross-domain retrieval system to bridge the gap between speech and music vi…

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2023

Toward Universal Text-To-Music Retrieval

Seungheon Doh, Minz Won, Keunwoo Choi, Juhan Nam

ICASSP 2023accepted

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 tags, unseen tags, and sentence-level descriptions. In reality, most previous works mainly focused on a single query type (…

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2018

Similarity Measures for Vocal-Based Drum Sample Retrieval Using Deep Convolutional Auto-Encoders

Adib Mehrabi, Keunwoo Choi, Simon Dixon, Mark B. Sandler

ICASSP 2018accepted

The expressive nature of the voice provides a powerful medium for communicating sonic ideas, motivating recent research on methods for query by vocalisation. Meanwhile, deep learning methods have demonstrated state-of-the-art results for matching vocal imitations to imitated sounds, yet little is kn…

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2017

Convolutional recurrent neural networks for music classification

Keunwoo Choi, György Fazekas, Mark B. Sandler, Kyunghyun Cho

ICASSP 2017accepted

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that…

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