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相关论文: Fusion of Audio and Visual Embeddings for Sound Ev…

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This study presents an audio-visual information fusion approach to sound event localization and detection (SELD) in low-resource scenarios. We aim at utilizing audio and video modality information through cross-modal learning and…

音频与语音处理 · 电气工程与系统科学 2024-06-24 Ya Jiang , Qing Wang , Jun Du , Maocheng Hu , Pengfei Hu , Zeyan Liu , Shi Cheng , Zhaoxu Nian , Yuxuan Dong , Mingqi Cai , Xin Fang , Chin-Hui Lee

This report presents our systems submitted to the audio-only and audio-visual tracks of the DCASE2025 Task 3 Challenge: Stereo Sound Event Localization and Detection (SELD) in Regular Video Content. SELD is a complex task that combines…

音频与语音处理 · 电气工程与系统科学 2025-07-08 Davide Berghi , Philip J. B. Jackson

In this study, we address the multimodal task of stereo sound event localization and detection with source distance estimation (3D SELD) in regular video content. 3D SELD is a complex task that combines temporal event classification with…

音频与语音处理 · 电气工程与系统科学 2025-09-09 Davide Berghi , Philip J. B. Jackson

This technical report details our work towards building an enhanced audio-visual sound event localization and detection (SELD) network. We build on top of the audio-only SELDnet23 model and adapt it to be audio-visual by merging both audio…

声音 · 计算机科学 2024-01-31 Adrian S. Roman , Baladithya Balamurugan , Rithik Pothuganti

Sound Event Detection and Localization (SELD) is a combined task of identifying sound events and their corresponding direction-of-arrival (DOA). While this task has numerous applications and has been extensively researched in recent years,…

声音 · 计算机科学 2024-06-13 Daniel Aleksander Krause , Archontis Politis , Annamaria Mesaros

This technical report outlines our approach to Task 3A of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024, focusing on Sound Event Localization and Detection (SELD). SELD provides valuable insights by estimating…

声音 · 计算机科学 2025-07-25 Quoc Thinh Vo , David Han

This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the challenge used four-channel audio formats of first-order…

This report describes our systems submitted for the DCASE2024 Task 3 challenge: Audio and Audiovisual Sound Event Localization and Detection with Source Distance Estimation (Track B). Our main model is based on the audio-visual (AV)…

音频与语音处理 · 电气工程与系统科学 2024-10-30 Davide Berghi , Philip J. B. Jackson

Sound Event Localization and Detection (SELD) is crucial in spatial audio processing, enabling systems to detect sound events and estimate their 3D directions. Existing SELD methods use single- or dual-branch architectures: single-branch…

声音 · 计算机科学 2025-07-31 Hogeon Yu

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 computationally generated spatial recordings to recordings of…

音频与语音处理 · 电气工程与系统科学 2022-09-12 Jinbo Hu , Yin Cao , Ming Wu , Qiuqiang Kong , Feiran Yang , Mark D. Plumbley , Jun Yang

In traditional sound event localization and detection (SELD) tasks, the focus is typically on sound event detection (SED) and direction-of-arrival (DOA) estimation, but they fall short of providing full spatial information about the sound…

声音 · 计算机科学 2025-01-22 Yuxuan Dong , Qing Wang , Hengyi Hong , Ya Jiang , Shi Cheng

Sound Event Detection and Localization (SELD) constitutes a complex task that depends on extensive multichannel audio recordings with annotated sound events and their respective locations. In this paper, we introduce a self-supervised…

音频与语音处理 · 电气工程与系统科学 2024-01-02 Orlem Lima dos Santos , Karen Rosero , Roberto de Alencar Lotufo

Sound event localization and detection (SELD) involves predicting active sound event classes over time while estimating their positions. The localization subtask in SELD is usually treated as a direction of arrival estimation problem,…

音频与语音处理 · 电气工程与系统科学 2026-04-21 Davide Berghi , Philip J. B. Jackson

Sound event localisation and detection (SELD) is a problem in the field of automatic listening that aims at the temporal detection and localisation (direction of arrival estimation) of sound events within an audio clip, usually of long…

Sound Event Localization and Detection (SELD) combines the Sound Event Detection (SED) with the corresponding Direction Of Arrival (DOA). Recently, adopted event oriented multi-track methods affect the generality in polyphonic environments…

声音 · 计算机科学 2026-02-02 Xueping Zhang , Yaxiong Chen , Ruilin Yao , Yunfei Zi , Shengwu Xiong

This technical report describes the systems submitted to the DCASE2022 challenge task 3: sound event localization and detection (SELD). The task aims to detect occurrences of sound events and specify their class, furthermore estimate their…

声音 · 计算机科学 2025-12-30 Jin Sob Kim , Hyun Joon Park , Wooseok Shin , Sung Won Han

Sound event localization and detection with source distance estimation (3D SELD) involves not only identifying the sound category and its direction-of-arrival (DOA) but also predicting the source's distance, aiming to provide full…

音频与语音处理 · 电气工程与系统科学 2024-11-22 Hengyi Hong , Qing Wang , Jun Du , Ruoyu Wei , Mingqi Cai , Xin Fang

Polyphonic sound event localization and detection (SELD), which jointly performs sound event detection (SED) and direction-of-arrival (DoA) estimation, detects the type and occurrence time of sound events as well as their corresponding DoA…

声音 · 计算机科学 2021-02-12 Yin Cao , Turab Iqbal , Qiuqiang Kong , Fengyan An , Wenwu Wang , Mark D. Plumbley

Sound event localization and detection (SELD) systems estimate both the direction-of-arrival (DOA) and class of sound sources over time. In the DCASE 2022 SELD Challenge (Task 3), models are designed to operate in a 4-channel setting. While…

Sound Event Localization and Detection (SELD) is a problem related to the field of machine listening whose objective is to recognize individual sound events, detect their temporal activity, and estimate their spatial location. Thanks to the…

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