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2 results for “sound event localization”
Sound event localization and detection (SELDnet) results
<p>This package is part of the work - <a href="https://github.com/sharathadavanne/seld-metric">Joint Measurement of Localization and Detection of Sound Events</a> presented in WASPAA 2019.</p> <p>This package consists of results from the <a href="https://arxiv.org/abs/1905.08546">SELDnet method</a> for joint sound event localization and detection. The results corresponding to different training states of 5, 25 and 75 epochs are provided here. These results are for the four cross-validation splits of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset. The sound events in this dataset consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. This <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset is part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a> and can be downloaded <a href="https://zenodo.org/record/2599196#.XT_RmHUzaCg">here</a>.</p> <p>Each of the results folders consists of 400 files, corresponding to the results of the individual recordings of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset.</p> <p>This data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>mic_dev_5</em></strong>,<strong><em> mic_dev_25, </em></strong>and <strong><em>mic_dev_75</em></strong>, correspond to SELDnet results at 5, 25 and 75 epochs.</p> <p>Download the zip files and use your favorite compression tool to unzip these split zip files.</p>
SECL-UMONS DATABASE FOR SOUND EVENT CLASSIFICATION AND LOCALIZATION
<p>SECL-UMons is a dataset for sound event classification and localization in the context of office environments. The multichannel dataset is composed of 11 event classes recorded at several realistic positions in two different rooms. The dataset comprises two types of sequences according to the number of events in the sequence. 2662 unilabel sequences and 2724 multilabel sequences are recorded corresponding to a total of 5.24 hours.</p>
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