Common sounds in bedrooms (CSIBE) corpora for sound event recognition of domestic robots

Although sound event recognition attracted much attention in the scientific community, applications in the robotics domain have not been in the focus. A new database was published in this paper and classifiers were evaluated with this dataset to guide the future practical developments of domestic robots. A corpus (CSIBE-RAW) was collected from the internet to build acoustic models to recognize 13 sound events and omit ambient sounds. As a case study, CSIBE-RAW was rerecorded in four room settings (CSIBE-AIBO) to create reverberation-tolerant classifiers for a Sony ERS-7. After eight classifiers were reviewed, the convolutional neural network achieved the best accuracy (95.07%) after multi-conditional learning and it was suitable for real-time classification on the robot. The effects of lossy audio codecs were studied, lossy encoder-tolerant audio statistics were specified for the feature vector and the Ogg Vorbis encoder with 128 kbit VBR was found superior to store big data and avoid any significant accuracy loss with the compression ratio 1:8.