Kahl, StefanHussein, HusseinFabian, EtienneSchloßhauer, JanThangaraju, EnniyanKowerko, DannyEibl, MaximilianEibl, MaximilianGaedke, Martin2017-08-282017-08-282017978-3-88579-669-5The classification of human-made acoustic events is important for the monitoring and recognition of human activities or critical behavior. In our experiments on acoustic event classification for the utilization in the sector of health care, we defined different acoustic events which represent critical events for elderly or people with disabilities in ambient assisted living environments or patients in hospitals. This contribution presents our work for acoustic event classification using deep learning techniques. We implemented and trained various convolutional neural networks for the extraction of deep feature vectors making use of current best practices in neural network design to establish a baseline for acoustic event classification. We convert chunks of audio signals into magnitude spectrograms and treat acoustic events as images. Our data set contains 20 different acoustic events which were collected in two different recording sessions combining human and environmental sounds. Our results demonstrate how efficient convolutional neural networks perform in the domain of acoustic event classification.enAcoustic Event ClassificationAcoustic Event DetectionConvolutional Neural Networks10.18420/in2017_2171617-5468