754 citations · 3.2k across the 112 of their papers we have counts for
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Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +4
Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular a…
Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
Jinhua Liang, Xubo Liu, Wenwu Wang +3
The auditory system plays a substantial role in shaping the overall human perceptual experience. While prevailing large language models (LLMs) and visual language models (VLMs) hav…
AudioSR: Versatile Audio Super-resolution at Scale
Haohe Liu, Ke Chen, Qiao Tian +2
Audio super-resolution is a fundamental task that predicts high-frequency components for low-resolution audio, enhancing audio quality in digital applications. Previous methods hav…
Retrieval-Augmented Text-to-Audio Generation
Yi Yuan, Haohe Liu, Xubo Liu +3
Despite recent progress in text-to-audio (TTA) generation, we show that the state-of-the-art models, such as AudioLDM, trained on datasets with an imbalanced class distribution, su…
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +5
For learning-based sound event localization and detection (SELD) methods, different acoustic environments in the training and test sets may result in large performance differences…
AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining
Haohe Liu, Yi Yuan, Xubo Liu +7
Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful considerat…