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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Hong-Hengyi/MVANet-SELD
공식 코드https://github.com/andresperezlopez/DCASE2019_task3
공식 코드A hybrid parametric-deep learning approach for sound event localization and detection
https://github.com/aromanusc/soundq
공식 코드Enhanced Sound Event Localization and Detection in Real 360-degree audio-visual soundscapes
https://github.com/axeber01/ngcc-seld
공식 코드Learning Multi-Target TDOA Features for Sound Event Localization and Detection
https://github.com/dberghi/av-seld
공식 코드Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection
https://github.com/dberghi/seld-distance-features
공식 코드Reverberation-based Features for Sound Event Localization and Detection with Distance Estimation
https://github.com/deepmind/perception_test
공식 코드Perception Test: A Diagnostic Benchmark for Multimodal Video Models
https://github.com/gzhu06/PodcastFillers_Utils
공식 코드Filler Word Detection and Classification: A Dataset and Benchmark
https://github.com/iranroman/spatialscaper
공식 코드https://github.com/ispamm/dualqseld-tcn
공식 코드Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation
https://github.com/itsjunwei/realtime-seld-edge
공식 코드Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices
https://github.com/jinbo-hu/dcase2022-task3
공식 코드https://github.com/jinbo-hu/pseldnets
공식 코드https://github.com/jinbo-hu/seld-data-generator
공식 코드https://github.com/l3das/l3das22
공식 코드L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment
https://github.com/muuda/MFF-EINV2
공식 코드https://github.com/nttrd-mdlab/seld-foa-meir
공식 코드Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments
https://github.com/nttrd-mdlab/wearable-seld-dataset
공식 코드https://github.com/orlllem/seld_wav2vec2
공식 코드https://github.com/rfalcon100/Spatial-Mixup-Pytorch
공식 코드https://github.com/sadPororo/AD-YOLO
공식 코드AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection
https://github.com/sharathadavanne/seld-dcase2021
공식 코드https://github.com/sharathadavanne/seld-dcase2022
공식 코드https://github.com/sony/audio-visual-seld-dcase2023
공식 코드https://github.com/sunhongze/STL-SNN
공식 코드A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks
https://github.com/thomeou/SALSA
공식 코드https://github.com/thomeou/salsa-lite
공식 코드https://github.com/JiuSenso/SELD-TCN
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks
https://github.com/Machine-Listeners-Valencia/seld-dcase2021
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
https://github.com/giusenso/seld-tcn
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks
https://github.com/prerak23/dir_srcmic_doa
https://github.com/sharathadavanne/seld-dcase2020
https://github.com/sharathadavanne/seld-dcase2023
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
https://github.com/yinkalario/EIN-SELD
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.