15 citations · 27 across the 22 of their papers we have counts for
23 papers
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…
Local Multimodal Music Alignment from Global Supervision
Irmak Bukey, Zachary Novack, Jongmin Jung +2
Understanding music requires understanding localized relationships across data modalities, e.g., how time in performance audio maps onto position in a score image. Yet supervision…
Parcae: Scaling Laws For Stable Looped Language Models
Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick +1
Traditional fixed-depth architectures scale quality by increasing training FLOPs, typically through increased parameterization, at the expense of a higher memory footprint, or data…
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…
Steering Autoregressive Music Generation with Recursive Feature Machines
Daniel Zhao, Daniel Beaglehole, Taylor Berg-Kirkpatrick +2
Controllable music generation remains a significant challenge, with existing methods often requiring model retraining or introducing audible artifacts. We introduce MusicRFM, a fra…
Zephyrus: An Agentic Framework for Weather Science
Sumanth Varambally, Marshall Fisher, Jas Thakker +14
Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lac…
MusiCRS: Benchmarking Audio-Centric Conversational Recommendation
Rohan Surana, Amit Namburi, Gagan Mundada +4
Conversational recommendation has advanced rapidly with large language models (LLMs), yet music remains a uniquely challenging domain in which effective recommendations require rea…
WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning
Gagan Mundada, Yash Vishe, Amit Namburi +4
Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, their reasoning abilities in th…
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…
Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues
Chinmay Talegaonkar, Nikhil Gandudi Suresh, Zachary Novack +3
Recent monocular metric depth estimation (MMDE) methods have made notable progress towards zero-shot generalization. However, they still exhibit a significant performance drop on o…
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…
Are you really listening? Boosting Perceptual Awareness in Music-QA Benchmarks
Yongyi Zang, Sean O'Brien, Taylor Berg-Kirkpatrick +2
Large Audio Language Models (LALMs), where pretrained text LLMs are finetuned with audio input, have made remarkable progress in music understanding. However, current evaluation me…
Aligning Text-to-Music Evaluation with Human Preferences
Yichen Huang, Zachary Novack, Koichi Saito +5
Despite significant recent advances in generative acoustic text-to-music (TTM) modeling, robust evaluation of these models lags behind, relying in particular on the popular Fréchet…
Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora
Ilana Shapiro, Ruanqianqian Huang, Zachary Novack +5
Western music is an innately hierarchical system of interacting levels of structure, from fine-grained melody to high-level form. In order to analyze music compositions holisticall…
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…
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…
DITTO: Diffusion Inference-Time T-Optimization for Music Generation
Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick +1
We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing…
Unsupervised Lead Sheet Generation via Semantic Compression
Zachary Novack, Nikita Srivatsan, Taylor Berg-Kirkpatrick +1
Lead sheets have become commonplace in generative music research, being used as an initial compressed representation for downstream tasks like multitrack music generation and autom…
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Zachary Novack, Julian McAuley, Zachary C. Lipton +1
Open vocabulary models (e.g. CLIP) have shown strong performance on zero-shot classification through their ability generate embeddings for each class based on their (natural langua…
Disentangling the Mechanisms Behind Implicit Regularization in SGD
Zachary Novack, Simran Kaur, Tanya Marwah +2
A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD)leads to improved generalization over the full-batch regime, with re…