Transcript Hierarchical classification tree

Audio Signal Feature Extraction and
Classification
Using Local Discriminant Bases
Karthikeyan Umapathy, Student Member, IEEE, Sridhar Krishnan, Senior Member, IEEE, and
Raveendra K. Rao, Senior Member, IEEE
Hierarchical classification tree
TECHNIQUE FLOW
• The audio classes were compared by taking two classes at a time.
• The training signals for each of the class within the pair of classes
were decomposed into wavelet packet trees.
• The corresponding nodes of the trees were compared using a set of
dissimilarity measures to identify the nodes that exhibit high
discriminative values between the classes.
• The nodes exhibiting high average discriminatory value among all the
pairwise combination over a number of trials were selected as the
final LDB nodes.
• The process can be repeated with different wavelets, and the wavelet
that exhibits overall better discrimination between classes can be
chosen as the best wavelet basis.
• Having selected the best wavelet basis and the significant LDB nodes,
a new wavelet packet tree was constructed.
• All the signals from the database (including the training signals) were
then decomposed using this new wavelet packet tree.
• Features were extracted from the LDB node basis vector coefficients
followed by classification using a linear discriminant analysis (LDA)based classifier.
• A hierarchical classification was performed starting from the first level
two groups to third level ten subgroups
MFCC features only
LDB features only
MFCC and LDB features