15 citations · 48 across the 13 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…