AI-Based, Automated Acoustic Diagnostics in Vehicles
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Publisher
Springer Fachmedien Wiesbaden
Link
https://link.springer.com/content/pdf/10.1007/978-3-658-41475-7_2
Reference9 articles.
1. Transfeld, P.; Receveur, S.; Fingscheidt, T.: An Acoustic Event Detection Framework and Evaluation Metric for Surveillance in Cars. In Proc. of Interspeech, Dresden, Sept. 2015, pp. 2927–2931
2. Matheja, T.; Buck, M.; Fingscheidt, T.: A Dynamic Multi-Channel Speech Enhancement System for Distributed Microphones in a Car Environment. In: EURASIP J. Adv. Signal Process. Vol. 2013/1, Dec. 2013, pp. 1–21
3. Cakir, E.; Parascandolo, G.; Heittola, T.; Huttunen, H.; Virtanen, T.: Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection. In: IEEE/ACM Trans. on ASLP. Vol. 25/6, June 2017, pp. 1291–1303
4. Strake, M.; Defraene, B.; Fluyt, K.; Tirry, W.; Fingscheidt, T.: Fully Convolutional Recurrent Networks for Speech Enhancement. In Proc. of ICASSP, Barcelona, May 2020, pp. 6674–6678
5. Baumann, J.; Lohrenz, T.; Roy, A.; Fingscheidt, T.: Beyond the DCASE 2017 Challenge on Rare Sound Event Detection: A Proposal for a More Realistic Training and Test Framework. In Proc. of ICASSP, Barcelona, May 2020, pp. 611–615
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