8 citations · 19 across the 20 of their papers we have counts for
13 papers · 1 filter
JazzSAMBA: A Synchronous and Asynchronous Multi-take Band Audio Dataset of Jazz Standards for Live Music Models
Phillip Long, Jacob Nguyen, Jace Hosto +9
Machine learning has made strong progress on music tasks, both as assistive tools and as creative partners. However, most systems train on multitrack corpora that emphasize pop and…
MusPyExpress: Extending MusPy with Enhanced Expression Text Support
Phillip Long, Hao-Wen Dong, Julian McAuley +1
Current work in modeling symbolic music primarily relies on representations extracted from MIDI-like data. While such formats allow for modeling symbolic music as sequences of note…
Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections
Haven Kim, Zachary Novack, Julian McAuley +1
Video-to-music generation has drawn growing interest for its role in conveying the emotion of visual media, including film. Progress in the field, however, is hampered by a reprodu…
MuseTok: Symbolic Music Tokenization for Generation and Semantic Understanding
Jingyue Huang, Zachary Novack, Phillip Long +4
Discrete representation learning has shown promising results across various domains, including generation and understanding in image, speech and language. Inspired by these advance…
WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling
Qihui Yang, Taylor Berg-Kirkpatrick, Julian McAuley +1
Despite rapid progress in end-to-end AI music generation, AI-driven modeling of professional Digital Signal Processing (DSP) workflows remains challenging. In particular, while the…
Video-Guided Text-to-Music Generation Using Public Domain Movie Collections
Haven Kim, Zachary Novack, Weihan Xu +2
Despite recent advancements in music generation systems, their application in film production remains limited, as they struggle to capture the nuances of real-world filmmaking, whe…
Fast Text-to-Audio Generation with Adversarial Post-Training
Zachary Novack, Zach Evans, Zack Zukowski +8
Text-to-audio systems, while increasingly performant, are slow at inference time, thus making their latency unpractical for many creative applications. We present Adversarial Relat…
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation
Junda Wu, Warren Li, Zachary Novack +3
Modeling temporal characteristics plays a significant role in the representation learning of audio waveform. We propose Contrastive Long-form Language-Audio Pretraining (\textbf{Co…
Presto! Distilling Steps and Layers for Accelerating Music Generation
Zachary Novack, Ge Zhu, Jonah Casebeer +3
Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration…
Generating Symbolic Music from Natural Language Prompts using an LLM-Enhanced Dataset
Weihan Xu, Julian McAuley, Taylor Berg-Kirkpatrick +2
Recent years have seen many audio-domain text-to-music generation models that rely on large amounts of text-audio pairs for training. However, symbolic-domain controllable music ge…
PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing
Phillip Long, Zachary Novack, Taylor Berg-Kirkpatrick +1
The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and la…
Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation
Junda Wu, Zachary Novack, Amit Namburi +5
Existing music captioning methods are limited to generating concise global descriptions of short music clips, which fail to capture fine-grained musical characteristics and time-aw…
DITTO-2: Distilled Diffusion Inference-Time T-Optimization for Music Generation
Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick +1
Controllable music generation methods are critical for human-centered AI-based music creation, but are currently limited by speed, quality, and control design trade-offs. Diffusion…