754 citations · 3.2k across the 112 of their papers we have counts for
63 papers · 1 filter
The Sounds of Home: A Speech-Removed Residential Audio Dataset for Sound Event Detection
Gabriel Bibbó, Thomas Deacon, Arshdeep Singh +1
This paper presents a residential audio dataset to support sound event detection research for smart home applications aimed at promoting wellbeing for older adults. The dataset is…
FlowSep: Language-Queried Sound Separation with Rectified Flow Matching
Yi Yuan, Xubo Liu, Haohe Liu +2
Language-queried audio source separation (LASS) focuses on separating sounds using textual descriptions of the desired sources. Current methods mainly use discriminative approaches…
Efficient Audio Captioning with Encoder-Level Knowledge Distillation
Xuenan Xu, Haohe Liu, Mengyue Wu +2
Significant improvement has been achieved in automated audio captioning (AAC) with recent models. However, these models have become increasingly large as their performance is enhan…
Sound-VECaps: Improving Audio Generation with Visual Enhanced Captions
Yi Yuan, Dongya Jia, Xiaobin Zhuang +9
Generative models have shown significant achievements in audio generation tasks. However, existing models struggle with complex and detailed prompts, leading to potential performan…
SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound
Haohe Liu, Xuenan Xu, Yi Yuan +3
Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelli…
T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining
Yi Yuan, Zhuo Chen, Xubo Liu +6
Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classificatio…
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…
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…
Compressing audio CNNs with graph centrality based filter pruning
James A King, Arshdeep Singh, Mark D. Plumbley
Convolutional neural networks (CNNs) are commonplace in high-performing solutions to many real-world problems, such as audio classification. CNNs have many parameters and filters,…
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…
ASiT: Local-Global Audio Spectrogram vIsion Transformer for Event Classification
Sara Atito, Muhammad Awais, Wenwu Wang +2
Transformers, which were originally developed for natural language processing, have recently generated significant interest in the computer vision and audio communities due to thei…
Automated Audio Captioning via Fusion of Low- and High- Dimensional Features
Jianyuan Sun, Xubo Liu, Xinhao Mei +3
Automated audio captioning (AAC) aims to describe the content of an audio clip using simple sentences. Existing AAC methods are developed based on an encoder-decoder architecture t…
Learning Temporal Resolution in Spectrogram for Audio Classification
Haohe Liu, Xubo Liu, Qiuqiang Kong +2
The audio spectrogram is a time-frequency representation that has been widely used for audio classification. One of the key attributes of the audio spectrogram is the temporal reso…
Surrey System for DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection with Segment-level Metric Learning
Haohe Liu, Xubo Liu, Xinhao Mei +3
Few-shot audio event detection is a task that detects the occurrence time of a novel sound class given a few examples. In this work, we propose a system based on segment-level metr…
Continual Learning For On-Device Environmental Sound Classification
Yang Xiao, Xubo Liu, James King +4
Continuously learning new classes without catastrophic forgetting is a challenging problem for on-device environmental sound classification given the restrictions on computation re…
A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +4
Polyphonic sound event localization and detection (SELD) aims at detecting types of sound events with corresponding temporal activities and spatial locations. In this paper, a trac…
ARCA23K: An audio dataset for investigating open-set label noise
Turab Iqbal, Yin Cao, Andrew Bailey +2
The availability of audio data on sound sharing platforms such as Freesound gives users access to large amounts of annotated audio. Utilising such data for training is becoming inc…
Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection under Domain-Shift Conditions
Andres Fernandez, Mark D. Plumbley
The goal of Unsupervised Anomaly Detection (UAD) is to detect anomalous signals under the condition that only non-anomalous (normal) data is available beforehand. In UAD under Doma…