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.
Keywords
Linear discriminant analysis
Principal component analysis
Feature extraction
Mel-frequency cepstrum
Pattern recognition (psychology)
Computer science
Artificial intelligence
Feature (linguistics)
Statistical classification
Dimensionality reduction
Speech recognition
k-nearest neighbors algorithm
Discriminant
Subject Areas
Speech and Audio Processing
·Signal Processing ·Physical Sciences
Music and Audio Processing
·Signal Processing ·Physical Sciences
Speech Recognition and Synthesis
·Artificial Intelligence ·Physical Sciences
Citations by Year
2020: 1
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