8 citations · 17 across the 11 of their papers we have counts for
12 papers
Learning Jazz Pianist Style with Cross-Attention Conditioning
Drew Edwards, Akira Maezawa, Simon Dixon
Jazz pianists develop distinctive traits that experienced listeners can often identify within seconds, yet the features underlying this recognition resist formal description. We st…
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…
SPHERE: Automatic Music Upmixing via Audio Language Model Post-Training with Spatial Heuristic Rewards
Zixun Guo, Calvin Murdock, Sanjeel Parekh +4
In this paper, we study the task of automatic music upmixing, wherein a system predicts spatial mixing parameters from a multi-stem recording. Different from existing methods that…
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…
Foundation Models for Music: A Survey
Yinghao Ma, Anders Øland, Anton Ragni +39
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This com…
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…
Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning
Yixiao Zhang, Yukara Ikemiya, Woosung Choi +7
Recent advances in text-to-music editing, which employ text queries to modify music (e.g.\ by changing its style or adjusting instrumental components), present unique challenges an…
A Data-Driven Analysis of Robust Automatic Piano Transcription
Drew Edwards, Simon Dixon, Emmanouil Benetos +2
Algorithms for automatic piano transcription have improved dramatically in recent years due to new datasets and modeling techniques. Recent developments have focused primarily on a…
MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
Yixiao Zhang, Yukara Ikemiya, Gus Xia +5
Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, music generation usually involves iterative refinements, and how to edit…
Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing
Yixiao Zhang, Akira Maezawa, Gus Xia +2
Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To addres…
GAN-based Generation and Automatic Selection of Explanations for Neural Networks
Saumitra Mishra, Daniel Stoller, Emmanouil Benetos +2
One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising the model input (e…