Please use this identifier to cite or link to this item:
Full metadata record
|dc.identifier.citation||IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol.2017-December, , pp.1812-1816||en_US|
|dc.description.abstract||In 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.||en_US|
|dc.title||Acoustic event classification using graph signals||en_US|
|Appears in Collections:||2. Conference Papers|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.