138 citations · 1.2k across the 72 of their papers we have counts for
21 papers · 1 filter
SAID: Semantic Acoustic Imaging Detector for Sound Event Localization and Detection
Runbang Wang, Zining Liang, Yin Cao +1
In daily life, people hear speech, footsteps, and music around them. We can often recognize these sounds and judge where they come from. Each sound source can be shown on a separat…
Region-Specific Audio Tagging for Spatial Sound
Jinzheng Zhao, Yong Xu, Haohe Liu +6
Audio tagging aims to label sound events appearing in an audio recording. In this paper, we propose region-specific audio tagging, a new task which labels sound events in a given r…
Universal Sound Separation with Self-Supervised Audio Masked Autoencoder
Junqi Zhao, Xubo Liu, Jinzheng Zhao +4
Universal sound separation (USS) is a task of separating mixtures of arbitrary sound sources. Typically, universal separation models are trained from scratch in a supervised manner…
WavCraft: Audio Editing and Generation with Large Language Models
Jinhua Liang, Huan Zhang, Haohe Liu +7
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing. Specifically,…
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…
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…
Multi-level Temporal-channel Speaker Retrieval for Zero-shot Voice Conversion
Zhichao Wang, Liumeng Xue, Qiuqiang Kong +4
Zero-shot voice conversion (VC) converts source speech into the voice of any desired speaker using only one utterance of the speaker without requiring additional model updates. Typ…
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…
Ontology-aware Learning and Evaluation for Audio Tagging
Haohe Liu, Qiuqiang Kong, Xubo Liu +3
This study defines a new evaluation metric for audio tagging tasks to overcome the limitation of the conventional mean average precision (mAP) metric, which treats different kinds…
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
Yuanzhe Chen, Ming Tu, Tang Li +7
Streaming voice conversion (VC) is the task of converting the voice of one person to another in real-time. Previous streaming VC methods use phonetic posteriorgrams (PPGs) extracte…
Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention
Xubo Liu, Qiushi Huang, Xinhao Mei +10
Audio captioning aims to generate text descriptions of audio clips. In the real world, many objects produce similar sounds. How to accurately recognize ambiguous sounds is a major…
Simple Pooling Front-ends For Efficient Audio Classification
Xubo Liu, Haohe Liu, Qiuqiang Kong +3
Recently, there has been increasing interest in building efficient audio neural networks for on-device scenarios. Most existing approaches are designed to reduce the size of audio…
Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains
Jinbo Hu, Yin Cao, Ming Wu +4
Sound event localization and detection (SELD) is a joint task of sound event detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of data transform from compu…
Segment-level Metric Learning for Few-shot Bioacoustic Event Detection
Haohe Liu, Xubo Liu, Xinhao Mei +3
Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel sound given a few examples. Previous methods employ metric learning to build a latent spa…
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
Haohe Liu, Xubo Liu, Qiuqiang Kong +5
Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on a single type of distortion, such as speech denoising or dereverberation. However, sp…
Separate What You Describe: Language-Queried Audio Source Separation
Xubo Liu, Haohe Liu, Qiuqiang Kong +5
In this paper, we introduce the task of language-queried audio source separation (LASS), which aims to separate a target source from an audio mixture based on a natural language qu…
Neural Vocoder is All You Need for Speech Super-resolution
Haohe Liu, Woosung Choi, Xubo Liu +3
Speech super-resolution (SR) is a task to increase speech sampling rate by generating high-frequency components. Existing speech SR methods are trained in constrained experimental…
Event-Independent Network for Polyphonic Sound Event Localization and Detection
Yin Cao, Turab Iqbal, Qiuqiang Kong +3
Polyphonic sound event localization and detection is not only detecting what sound events are happening but localizing corresponding sound sources. This series of tasks was first i…
Audio Tagging by Cross Filtering Noisy Labels
Boqing Zhu, Kele Xu, Qiuqiang Kong +2
High quality labeled datasets have allowed deep learning to achieve impressive results on many sound analysis tasks. Yet, it is labor-intensive to accurately annotate large amount…
Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks
Qiuqiang Kong, Yong Xu, Wenwu Wang +2
Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior k…
Multi-level Attention Model for Weakly Supervised Audio Classification
Changsong Yu, Karim Said Barsim, Qiuqiang Kong +1
In this paper, we propose a multi-level attention model to solve the weakly labelled audio classification problem. The objective of audio classification is to predict the presence…