414 citations · 763 across the 23 of their papers we have counts for
7 papers · 1 filter
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift
Jisheng Bai, Mou Wang, Haohe Liu +11
Acoustic scene classification (ASC) is a crucial research problem in computational auditory scene analysis, and it aims to recognize the unique acoustic characteristics of an envir…
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…
Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning
Jianyuan Sun, Xubo Liu, Xinhao Mei +3
Automated audio captioning (AAC) which generates textual descriptions of audio content. Existing AAC models achieve good results but only use the high-dimensional representation of…
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…
Low-complexity CNNs for Acoustic Scene Classification
Arshdeep Singh, James A King, Xubo Liu +2
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC…
Low-complexity CNNs for Acoustic Scene Classification
Arshdeep Singh, Mark D. Plumbley
This paper presents a low-complexity framework for acoustic scene classification (ASC). Most of the frameworks designed for ASC use convolutional neural networks (CNNs) due to thei…