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A dataset for high-level activity recognition based on low level audio events

Theodoros Giannakopoulos; Stasinos Konstantopoulos


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    "description": "<p>The high level activities are:<br>\n\u00a0- kitchencleanup<br>\n\u00a0- music<br>\n\u00a0- no activity<br>\n\u00a0- other activity<br>\n\u00a0- talk<br>\n\u00a0- tv</p>\n\n<p>Each recording of low-level audio events is stored in a separate file.</p>\n\n<p>Files are organized in 6 folders, each folder corresponding to a separate file.</p>\n\n<p>The format of is file is json-like. In particular, each row has the following format:</p>\n\n<p>{\"prob\": 0.88557562121157585, \"energy\": 0.024511212402412885, \"t\": 1485110417, \"event\": \"speech\"}</p>\n\n<p>This dataset can be evaluated with the python code metaClassifier/evaluate.py of the AUOR repository:<br>\nhttps://github.com/tyiannak/AUROS</p>", 
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    "title": "A dataset for high-level activity recognition based on low level audio events", 
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