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This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge. The goal of the SELD task is to detect the temporal activities of a known set of sound event classes, and further localize them…

声音 · 计算机科学 2019-05-27 Sharath Adavanne , Archontis Politis , Tuomas Virtanen

This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD). The dataset is based on emulation of real recordings of static or moving sound events under real conditions…

音频与语音处理 · 电气工程与系统科学 2021-07-06 Archontis Politis , Sharath Adavanne , Daniel Krause , Antoine Deleforge , Prerak Srivastava , Tuomas Virtanen

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

Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. As a result, SELD inherits the challenges of both tasks, such as noise,…

音频与语音处理 · 电气工程与系统科学 2021-10-28 Thi Ngoc Tho Nguyen , Karn N. Watcharasupat , Zhen Jian Lee , Ngoc Khanh Nguyen , Douglas L. Jones , Woon Seng Gan

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…

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…

This technical report details our systems submitted for Task 3 of the DCASE 2024 Challenge: Audio and Audiovisual Sound Event Localization and Detection (SELD) with Source Distance Estimation (SDE). We address only the audio-only SELD with…

音频与语音处理 · 电气工程与系统科学 2024-07-15 Jun Wei Yeow , Ee-Leng Tan , Jisheng Bai , Santi Peksi , Woon-Seng Gan

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

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

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

In this paper, we describe our method for DCASE2019 task3: Sound Event Localization and Detection (SELD). We use four CRNN SELDnet-like single output models which run in a consecutive manner to recover all possible information of occurring…

音频与语音处理 · 电气工程与系统科学 2019-10-31 Sławomir Kapka , Mateusz Lewandowski

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

Sound event detection (SED) entails identifying the type of sound and estimating its temporal boundaries from acoustic signals. These events are uniquely characterized by their spatio-temporal features, which are determined by the way they…

音频与语音处理 · 电气工程与系统科学 2023-05-19 Tanmay Khandelwal , Rohan Kumar Das

Existing systems for sound event localization and detection (SELD) typically operate by estimating a source location for all classes at every time instant. In this paper, we propose an alternative class-conditioned SELD model for situations…

音频与语音处理 · 电气工程与系统科学 2022-03-09 Olga Slizovskaia , Gordon Wichern , Zhong-Qiu Wang , Jonathan Le Roux

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

We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We analyze the performance of the submissions to DCASE 2021 task 4…

Sound event localization and detection (SELD) aims to determine the appearance of sound classes, together with their Direction of Arrival (DOA). However, current SELD systems can only predict the activities of specific classes, for example,…

音频与语音处理 · 电气工程与系统科学 2024-06-25 Jinzheng Zhao , Xinyuan Qian , Yong Xu , Haohe Liu , Yin Cao , Davide Berghi , Wenwu Wang

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

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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