# Sound source detection, localization and classification using consecutive ensemble of CRNN models Source: [Sound source detection, localization and classification using consecutive ensemble of CRNN models]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:53:21.106Z Authors: S{\\l}awomir Kapka, Mateusz Lewandowski Published: 2019-08-02T09:10:08.000Z ## Abstract 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 events. We decompose the SELD task into estimating number of active sources, estimating direction of arrival of a single source, estimating direction of arrival of the second source where the direction of the first one is known and a multi-label classification task. We use custom consecutive ensemble to predict events' onset, offset, direction of arrival and class. The proposed approach is evaluated on the TAU Spatial Sound Events 2019 - Ambisonic and it is compared with other participants' submissions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Sound Event Localization and Detection on TAU Spatial DCASE Task 3 2019 (full evaluation)](): Sound Event Localization and Detection; TAU Spatial DCASE Task 3 2019 (full evaluation); F1 Score: 94.7. ## Original sources and implementation [Original paper]() [PDF]()