138 citations · 792 across the 57 of their papers we have counts for
19 papers · 1 filter
Balanced SNR-Aware Distillation for Guided Text-to-Audio Generation
Bingzhi Liu, Yin Cao, Haohe Liu +1
Diffusion models have demonstrated promising results in text-to-audio generation tasks. However, their practical usability is hindered by slow sampling speeds, limiting their appli…
First-Shot Unsupervised Anomalous Sound Detection With Unknown Anomalies Estimated by Metadata-Assisted Audio Generation
Hejing Zhang, Qiaoxi Zhu, Jian Guan +6
First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types…
Synth-AC: Enhancing Audio Captioning with Synthetic Supervision
Feiyang Xiao, Qiaoxi Zhu, Jian Guan +4
Data-driven approaches hold promise for audio captioning. However, the development of audio captioning methods can be biased due to the limited availability and quality of text-aud…
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…
Multimodal Fish Feeding Intensity Assessment in Aquaculture
Meng Cui, Xubo Liu, Haohe Liu +5
Fish feeding intensity assessment (FFIA) aims to evaluate fish appetite changes during feeding, which is crucial in industrial aquaculture applications. Existing FFIA methods are l…
MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies
Ke Chen, Yusong Wu, Haohe Liu +3
Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of…
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…
Separate Anything You Describe
Xubo Liu, Qiuqiang Kong, Yan Zhao +7
Language-queried audio source separation (LASS) is a new paradigm for computational auditory scene analysis (CASA). LASS aims to separate a target sound from an audio mixture given…
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…
Text-Driven Foley Sound Generation With Latent Diffusion Model
Yi Yuan, Haohe Liu, Xubo Liu +4
Foley sound generation aims to synthesise the background sound for multimedia content. Previous models usually employ a large development set with labels as input (e.g., single num…
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…
Adapting Language-Audio Models as Few-Shot Audio Learners
Jinhua Liang, Xubo Liu, Haohe Liu +4
We presented the Treff adapter, a training-efficient adapter for CLAP, to boost zero-shot classification performance by making use of a small set of labelled data. Specifically, we…
Learning to detect an animal sound from five examples
Inês Nolasco, Shubhr Singh, Veronica Morfi +14
Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behaviour. In the past twenty years, the volume of digitised wil…
Universal Source Separation with Weakly Labelled Data
Qiuqiang Kong, Ke Chen, Haohe Liu +4
Universal source separation (USS) is a fundamental research task for computational auditory scene analysis, which aims to separate mono recordings into individual source tracks. Th…
Latent Diffusion Model Based Foley Sound Generation System For DCASE Challenge 2023 Task 7
Yi Yuan, Haohe Liu, Xubo Liu +3
Foley sound presents the background sound for multimedia content and the generation of Foley sound involves computationally modelling sound effects with specialized techniques. In…
WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research
Xinhao Mei, Chutong Meng, Haohe Liu +6
The advancement of audio-language (AL) multimodal learning tasks has been significant in recent years. However, researchers face challenges due to the costly and time-consuming col…
Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study
Yi Yuan, Haohe Liu, Jinhua Liang +3
Deep neural networks have recently achieved breakthroughs in sound generation. Despite the outstanding sample quality, current sound generation models face issues on small-scale da…
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Haohe Liu, Zehua Chen, Yi Yuan +5
Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited gener…