754 citations · 3.2k across the 112 of their papers we have counts for
10 papers · 2 filters
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…
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…
WavJourney: Compositional Audio Creation with Large Language Models
Xubo Liu, Zhongkai Zhu, Haohe Liu +8
Despite breakthroughs in audio generation models, their capabilities are often confined to domain-specific conditions such as speech transcriptions and audio captions. However, rea…
Environmental Sound Classification on An Embedded Hardware Platform
Gabriel Bibbo, Arshdeep Singh, Mark D. Plumbley
Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on r…
E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks
Arshdeep Singh, Haohe Liu, Mark D. Plumbley
Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking. Recent machine learni…