A Dataset For High-Level Activity Recognition Based On Low Level Audio Events

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Theodoros Giannakopoulos ; Stasinos Konstantopoulos (2017)

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