Auflistung nach Schlagwort "Acoustic Event Classification"
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- TextdokumentAcoustic Event Classification Using Convolutional Neural Networks(INFORMATIK 2017, 2017) Kahl, Stefan; Hussein, Hussein; Fabian, Etienne; Schloßhauer, Jan; Thangaraju, Enniyan; Kowerko, Danny; Eibl, MaximilianThe 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.
- TextdokumentWS34 - Deep Learning in heterogenen Datenbeständen(INFORMATIK 2017, 2017) Kowerko, Danny; Kahl, StefanDeep learning techniques, especially artificial neural networks, have become irreplaceable in almost every aspect of modern information science. Breakthrough technologies evolve rapidly, driven by researchers with both, scientific and economic backgrounds. This workshop is a platform for students, post-docs, innovative enterprises and experts from Germany who present their latest works and demo applications. Recent advanced in the field of deep learning and their impact on research projects and economic endeavors are at the center of submitted papers and presentations. An active debate focusing on current work-in-progress, future research as well as chances and opportunities of deep learning is complemented by the discussion regarding the generation, processing and publishing of large heterogeneous datasets for research purposes. The presented contributions span a wide variety of deep learning applications – from robotics to audio and text retrieval, from human pose estimation to medical data processing. This not only demonstrates how important deep learning techniques have become for almost every area of research, it also shows the importance of scientific transparency.Without the efforts of countless researchers around the globe who published their work and complemented it with code repositories and extensive documentation, some of the presented applications could not have been implemented. This reminds us: An active deep learning community is vital for the success of innovative data processing routines and with that, forms the foundation of a steady evolution powered by scientific research. We would like to thank everyone who participated in this workshop, especially the authors and presenters who contributed to the success of this novel format.