Search Results for author:
Found 12 papers, 2 papers with code
Sound event localization and classification using WASN in Outdoor Environment
Moreover, methods using multiple microphone arrays often focus solely on source localization, neglecting the aspect of sound event classification.
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift
In addition, considering the abundance of unlabeled acoustic scene data in the real world, it is important to study the possible ways to utilize these unlabelled data.
Sub-band and Full-band Interactive U-Net with DPRNN for Demixing Cross-talk Stereo Music
This paper presents a detailed description of our proposed methods for the ICASSP 2024 Cadenza Challenge.
Interactive Dual-Conformer with Scene-Inspired Mask for Soft Sound Event Detection
In this paper, we first propose an interactive dual-conformer (IDC) module, in which a cross-interaction mechanism is applied to effectively exploit the information from soft labels.
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning
This paper presents AudioLog, a large language models (LLMs)-powered audio logging system with hybrid token-semantic contrastive learning.
Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement
3D speech enhancement can effectively improve the auditory experience and plays a crucial role in augmented reality technology.
Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework
Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well.
SSDPT: Self-Supervised Dual-Path Transformer for Anomalous Sound Detection in Machine Condition Monitoring
In this article, we propose a self-supervised dual-path Transformer (SSDPT) network to detect anomalous sounds in machine monitoring.
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition
Evaluations for the three tasks are conducted on the recent databases of detection and classification of acoustic scenes and event challenges.
A comparison of handcrafted, parameterized, and learnable features for speech separation
Among them, learnable features, which are trained with separation networks jointly in an end-to-end fashion, become a new trend of modern speech separation research, e. g. convolutional time domain audio separation network (Conv-Tasnet), while handcrafted and parameterized features are also shown competitive in very recent studies.
Sound
Multimodal Urban Sound Tagging with Spatiotemporal Context
Noise pollution significantly affects our daily life and urban development.
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model
This paper presents an unsupervised nonlinear spectral unmixing method based on a deep autoencoder network that applies to a generalized linear-mixture/nonlinear fluctuation model, consisting of a linear mixture component and an additive nonlinear mixture component that depends on both endmembers and abundances.
