4 citations · 9 across the 49 of their papers we have counts for
6 papers · 1 filter
AutoSIFT: Automatic Style Sifting for Controllable Speech Generation with Arbitrary Style Infilling
Haowei Lou, Junda Wu, Chengkai Huang +4
State-of-the-art text-to-speech (TTS) models achieve impressive naturalness and expressiveness, yet fine-grained, disentangled control over speaking styles remains challenging. In…
Evaluating Music Context Preservation: A Multi-facet Framework for Music Editing Systems
Yash Vishe, Eric Xue, Xunyi Jiang +4
Music editing plays a vital role in modern music production, with applications in film, broadcasting, and game development. Recent advances in music editing systems have enabled di…
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…
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…
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…