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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Byeong-jun Han Eenjun Hwang |
| Copyright Year | 2009 |
| Description | Author affiliation: School of Electrical Engineering, Korea University, Seoul, Korea (Byeong-jun Han; Eenjun Hwang) |
| Abstract | To date, common acoustic features such as MPEG-7 and Fourier/wavelet transform-based features have been frequently used for environmental sound classification. However, these transforms have difficulty dealing with specific properties of environmental sounds, due to their limited scopes. In this paper, we investigate three types of transforms as yet untried for this purpose, and show that they are more effective than traditional features. This result is mainly due to the fact that they have functionalities that were not easily treatable with traditional transforms. Experimental results show that the combination of these features with traditional features can achieve 86.09% of the maximum accuracy in environmental sound classification, compared to 74.35% of the maximum accuracy when confined to traditional features. |
| Starting Page | 542 |
| Ending Page | 545 |
| File Size | 488948 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424442904 |
| ISSN | 19457871 |
| DOI | 10.1109/ICME.2009.5202553 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Collaboration Discrete transforms Signal analysis Chirp Frequency domain analysis Speech recognition Feature extraction Hidden Markov models Principal component analysis Independent component analysis feature extraction Environmental sound recognition discrete chirplet transform discrete curvelet transform discrete Hilbert transform |
| Content Type | Text |
| Resource Type | Article |
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