Mulimani, M.Jahnavi, U.P.Koolagudi, S.G.2020-03-302020-03-302017IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol.2017-December, , pp.1812-1816https://idr.nitk.ac.in/handle/123456789/7186In this paper, a graph signal is generated from spectrogram and features are investigated from graph signal for Acoustic Event Classification (AEC). Different acoustic events are selected from Sound Scene Database of Real Word Computing Partnership (RWCP) group. Three different noises are selected from NOISEX'92 database and added to test samples at different noise conditions separately. The recognition performance of acoustic events using proposed features and Mel-frequency cepstral coefficients (MFCCs) with clean and noisy test samples are compared. The proposed features show significantly improved recognition accuracy over MFCCs in noisy conditions. � 2017 IEEE.Book chapter