Arunodhayan Sampath Kumar
ABSTRACT
In this paper we present a real-time demo for acoustic event classification using a Convolutional Neural Network (CNN). When an acoustic event is fed as input into our system in real-time, the system performs the classification task and denotes to which class the acoustic event belongs. We combined different audio datasets into an own one consisting of 94 classes belonging to the context of Ambient Assisted Living (AAL). The so-called AAL-94 audio set is a combination of publicly available ESC-50 [7], Audio Set [4] and Ultrasound-8k [8] datasets. We enriched these subsets with own laboratory recordings to create a collection of 18,882 audio recordings typical for AAL. The datasets were trained and the classification task is performed using a CNN. The best model from the training process has been snapshot and is used for real-time audio processing in our demo. The latter visualizes the audio classification results in a real-time spectrogram and some statistical plots. Users either interacts creating noises themselves from the 94 available classes shown on an auxiliary screen of the demo, or trigger sounds from a MIDI keyboard to test the system performance live. Current and overall classification results are demonstrated on the main screen.
Supplemental Material
- Rosa Maria Alsina-Pagè s, Joan Navarro, Francesc Al'i as, and Marcos Hervá s. 2017. homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring. Sensors , Vol. 17, 4 (2017), 854. https://doi.org/10.3390/s17040854Google Scholar
- Prerna Arora and Reinhold Haeb-Umbach. 2017. A study on transfer learning for acoustic event detection in a real life scenario. In 19th IEEE International Workshop on Multimedia Signal Processing, MMSP 2017, Luton, United Kingdom, October 16--18, 2017. 1--6. https://doi.org/10.1109/MMSP.2017.8122258Google ScholarCross Ref
- Paul M. Baggenstoss. 2018. Acoustic Event Classification Using Multi-Resolution HMM. In 26th European Signal Processing Conference, EUSIPCO 2018, Roma, Italy, September 3--7, 2018 . 972--976. https://doi.org/10.23919/EUSIPCO.2018.8553131Google Scholar
- Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R. Channing Moore, Manoj Plakal, and Marvin Ritter. 2017. Audio Set: An ontology and human-labeled dataset for audio events. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, March 5--9, 2017. 776--780. https://doi.org/10.1109/ICASSP.2017.7952261Google ScholarDigital Library
- Stefan Kahl, Hussein Hussein, Etienne Fabian, Jan Schloßhauer, Enniyan Thangaraju, Danny Kowerko, and Maximilian Eibl. 2017a. Acoustic Event Classification Using Convolutional Neural Networks. In INFORMATIK 2017 , , Maximilian Eibl and Martin Gaedke (Eds.). Gesellschaft für Informatik, Bonn, Chemnitz, Germany, 2177--2188. https://doi.org/10.18420/in2017_217Google Scholar
- Stefan Kahl, Thomas Wilhelm-Stein, Hussein Hussein, Holger Klinck, Danny Kowerko, Marc Ritter, and Maximilian Eibl. 2017b. Large-Scale Bird Sound Classification using Convolutional Neural Networks. In Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum, Dublin, Ireland, September 11--14, 2017. http://ceur-ws.org/Vol-1866/paper_143.pdfGoogle Scholar
- Karol J. Piczak. 2015. Environmental sound classification with convolutional neural networks. In 25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015, Boston, MA, USA, September 17--20, 2015. 1--6. https://doi.org/10.1109/MLSP.2015.7324337Google ScholarCross Ref
- Justin Salamon, Christopher Jacoby, and Juan Bello. 2014. A Dataset and Taxonomy for Urban Sound Research, In Proceedings of the ACM International Conference on Multimedia, MM '14, Orlando, FL, USA, November 03 - 07, 2014. Proceedings - 22nd ACM International Conference on Multimedia . https://doi.org/10.1145/2647868.2655045Google ScholarDigital Library
Index Terms
- A Real-Time Demo for Acoustic Event Classification in Ambient Assisted Living Contexts
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