15 citations · 48 across the 13 of their papers we have counts for
14 papers
Synthesis and editing of multi-instrument audio mixtures using scalar-quantised latents with MIDI Span conditioning
Sungkyun Chang, Keshav Bhandari, Simon Dixon +1
Music creation often involves iterative refinement, changing selected musical details while retaining the rest. To support such refinement, we introduce SpanSynth-Edit, a flow-matc…
Finetuning Strategies for Querying Sounds by Vocal Imitation
Aditya Bhattacharjee, Christos Plachouras, Sungkyun Chang +1
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 st…
Text2Score: Generating Sheet Music From Textual Prompts
Keshav Bhandari, Sungkyun Chang, Abhinaba Roy +4
Developing text-driven symbolic music generation models remains challenging due to the scarcity of aligned text-music datasets and the unreliability of automated captioning pipelin…
Advancing Multi-Instrument Music Transcription: Results from the 2025 AMT Challenge
Ojas Chaturvedi, Kayshav Bhardwaj, Tanay Gondil +5
This paper presents the results of the 2025 Automatic Music Transcription (AMT) Challenge, an online competition to benchmark progress in multi-instrument transcription. Eight team…
CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction
Yinghao Ma, Haiwen Xia, Hewei Gao +9
While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind. In this paper, we…
RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection
Sungkyun Chang, Simon Dixon, Emmanouil Benetos
This study introduces RUMAA, a transformer-based framework for music performance analysis that unifies score-to-performance alignment, score-informed transcription, and mistake det…
ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement
Keshav Bhandari, Sungkyun Chang, Tongyu Lu +4
Despite deep learning's remarkable advances in style transfer across various domains, generating controllable performance-level musical style transfer for complete symbolically rep…
YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation
Sungkyun Chang, Emmanouil Benetos, Holger Kirchhoff +1
Multi-instrument music transcription aims to convert polyphonic music recordings into musical scores assigned to each instrument. This task is challenging for modeling as it requir…
Offline Clustering Approach to Self-supervised Learning for Class-imbalanced Image Data
Hye-min Chang, Sungkyun Chang
Class-imbalanced datasets are known to cause the problem of model being biased towards the majority classes. In this project, we set up two research questions: 1) when is the class…
Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning
Sungkyun Chang, Donmoon Lee, Jeongsoo Park +4
Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation fro…
Sequential Skip Prediction with Few-shot in Streamed Music Contents
Sungkyun Chang, Seungjin Lee, Kyogu Lee
This paper provides an outline of the algorithms submitted for the WSDM Cup 2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the challenge, complete infor…
Audio Cover Song Identification using Convolutional Neural Network
Sungkyun Chang, Juheon Lee, Sang Keun Choe +1
In this paper, we propose a new approach to cover song identification using a CNN (convolutional neural network). Most previous studies extract the feature vectors that characteriz…
Lyrics-to-Audio Alignment by Unsupervised Discovery of Repetitive Patterns in Vowel Acoustics
Sungkyun Chang, Kyogu Lee
Most of the previous approaches to lyrics-to-audio alignment used a pre-developed automatic speech recognition (ASR) system that innately suffered from several difficulties to adap…
A pairwise approach to simultaneous onset/offset detection for singing voice using correntropy
Sungkyun Chang, Kyogu Lee
In this paper, we propose a novelmethod to search for precise locations of paired note onset and offset in a singing voice signal. In comparison with the existing onset detection a…