2020/09/30 by Archontis Politis, Annamaria Mesaros, Sharath Adavanne +2 · 135 citations
Computer Science · Engineering · #Artificial intelligence #Cartography #Computer science #Data mining #Engineering #Event (particle physics) #Geography #Joint (building) #Machine learning #Music and Audio Processing #Pattern recognition (psychology) #Ranking (information retrieval) #Scale (ratio) #Sound (geography) #Speech Recognition and Synthesis #Speech and Audio Processing #Task (project management) #cs.SD #eess.AS
paper · pdf · doi:10.1109/taslp.2020.3047233
published in IEEE/ACM Transactions on Audio Speech and Language Processing 29, 684-698 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2020/12/24 · openalex created_date 2021/01/05 · arxiv created 2021/01/11 · arxiv updated 2021/01/12 · openalex updated_date 2026/08/05
Sound event localization and detection is a novel area of research that emerged from the combined interest of analyzing the acoustic scene in terms of the spatial and temporal activity of sounds of interest. This paper presents an overview of the first international evaluation on sound event localization and detection, organized as a task of the DCASE 2019 Challenge. A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset. The overview presents in detail how the systems were evaluated and ranked and the characteristics of the best-performing systems. Common strategies in terms of input features, model architectures, training approaches, exploitation of prior knowledge, and data augmentation are discussed. Since ranking in the challenge was based on individually evaluating localization and event classification performance, part of the overview focuses on presenting metrics for the joint measurement of the two, together with a reevaluation of submissions using these new metrics. The new analysis reveals submissions that performed better on the joint task of detecting the correct type of event close to its original location than some of the submissions that were ranked higher in the challenge. Consequently, ranking of submissions which performed strongly when evaluated separately on detection or localization, but not jointly on both, was affected negatively.