Name
Abstract | ||
|---|---|---|
In this paper, by using a novel database of home environment warning sounds, the classification and recognition performances of these sounds are compared over feature extraction algorithms. Following the sample reduction of the feature vectors by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), k-Nearest Neighbour (k-NN) algorithm is employed for classification. Besides, a modified version of the algorithm for MF coefficients is proposed and we observe that the classification performance is better than MFCC and LPC even at low SNR values. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SIU.2015.7130263 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
LPC,MFCC,classification,home environment sound,warning sound | k-nearest neighbors algorithm,Dimensionality reduction,Pattern recognition,Computer science,Feature extraction,Sound classification,Speech recognition,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Calik, Nurullah | 1 | 2 | 2.42 |
| Durak Ata, Lutfiye | 2 | 0 | 0.34 |
| Serbes, Ahmet | 3 | 0 | 0.34 |
| Bolat, Bulent | 4 | 0 | 0.34 |