|NAR is a dataset of audio recordings made with the humanoid robot Nao in real world conditions for sound recognition benchmarking. All the recordings were collected using the robot’s microphone and thus have the following characteristics:
The dataset is available at this location : The NAR dataset (ZIP file, 35MB). The data are freely accessible for scientific research purposes and for non-commercial applications.
The dataset is organized as follows:
- Each class is represented by a folder containing all the audio files labeled with the class.
- The name of a folder is the name of the class attached. The name of an audio file is “foldername$id.wav” where $id is an incremental identifier starting at 1.
- Each audio file is provided in a WAV format (mono signal, 48kHz sampling rate and 16 bits per sample).
- 42 differents class for 852 sounds have been recorded and organized into four scenarios :
|Kitchen||Eating, Choking, Cuttlery, Fill a glass, Running the tap, Open/close a drawer,Move a chair, Open microwave,Close microwave, Microwave, Fridge, Toaster|
|Office||Door Close, Open, Key, Knock, Ripped Paper, Zip, (another) Zip|
|Nonverbal||Fingerclap, Handclap, Tongue Clic|
|Speech||1,2,3,4,5,6,7,8,9,10, Hello, Left, Right, Turn, Move, Stop, Nao, Yes, No, What|
- Maxime Janvier, Xavier Alameda-Pineda, Laurent Girin and Radu Horaud. Sound Representation and Classification Benchmark for Domestic Robots. IEEE International Conference on Robotics and Automation (ICRA’14), May-June 2014, Hong-Kong.
- Maxime Janvier, Xavier Alameda-Pineda, Laurent Girin and Radu Horaud. Sound-Event Recognition with a Companion Humanoid. IEEE International Conference on Humanoid Robotics (HUMANOIDS’12), November 2012, Osaka, Japan.