Sound Event Localization and Detection
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Given multichannel audio input, a sound event detection and localization (SELD) system outputs a temporal activation track for each of the target sound classes, along with one or more corresponding spatial trajectories when the track indicates activity. This results in a spatio-temporal characterization of the acoustic scene that can be used in a wide range of machine cognition tasks, such as inference on the type of environment, self-localization, navigation without visual input or with occluded targets, tracking of specific types of sound sources, smart-home applications, scene visualization systems, and audio surveillance, among others.
논문
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Stereo sound event localization and detection based on PSELDnet pretraining and BiMamba sequence modeling
CST-former: Multidimensional Attention-based Transformer for Sound Event Localization and Detection in Real Scenes
Reverberation-based Features for Sound Event Localization and Detection with Distance Estimation
An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation
MVANet: Multi-Stage Video Attention Network for Sound Event Localization and Detection with Source Distance Estimation
Class-Incremental Learning for Sound Event Localization and Detection
PSELDNets: Pre-trained Neural Networks on Large-scale Synthetic Datasets for Sound Event Localization and Detection
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection
Leveraging Reverberation and Visual Depth Cues for Sound Event Localization and Detection with Distance Estimation
Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices
Learning Spatially-Aware Language and Audio Embeddings
Learning Multi-Target TDOA Features for Sound Event Localization and Detection
SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation
Squeeze-and-Excite ResNet-Conformers for Sound Event Localization, Detection, and Distance Estimation for DCASE 2024 Challenge
Text-Queried Target Sound Event Localization
Exploring Audio-Visual Information Fusion for Sound Event Localization and Detection In Low-Resource Realistic Scenarios
MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection
6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human
Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality
BAT: Learning to Reason about Spatial Sounds with Large Language Models
Enhanced Sound Event Localization and Detection in Real 360-degree audio-visual soundscapes
Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms
Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection
CST-former: Transformer with Channel-Spectro-Temporal Attention for Sound Event Localization and Detection
Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection
w2v-SELD: A Sound Event Localization and Detection Framework for Self-Supervised Spatial Audio Pre-Training
Feature Aggregation in Joint Sound Classification and Localization Neural Networks
SwG-former: A Sliding-Window Graph Convolutional Network for Simultaneous Spatial-Temporal Information Extraction in Sound Event Localization and Detection
Leveraging Geometrical Acoustic Simulations of Spatial Room Impulse Responses for Improved Sound Event Detection and Localization
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection
Dynamic Kernel Convolution Network with Scene-dedicate Training for Sound Event Localization and Detection
STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events
Divided spectro-temporal attention for sound event localization and detection in real scenes for DCASE2023 challenge
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.