SPEECH/MUSIC DISCRIMINATION BASED ON WARPING TRANSFORMATION AND FUZZY LOGIC FOR INTELLIGENT AUDIO CODING

Figures & data

FIGURE 1 Example illustrating the values that LPC-SC and WLPC-SC takes for both speech and music signals.

FIGURE 2 A two-class example of PCA and LDA transformations.

FIGURE 3 Expert system general structure.

FIGURE 4 Membership functions for input variables.

FIGURE 5 Membership functions for the output variable.

TABLE 1 Classification Accuracy Percentage. WLPC-SC vs. Timbral Features

Feature	Speech (%)	Music (%)	Global (%)
SC	90.67	90.0	90.33
SR	89.88	89.64	89.76
SF	80.77	89.44	84.72
ZC	90.28	83.82	87.05
MFCC	92.85	91.64	92.24
LE	89.29	81.45	85.37
WLPC-SC	94.07	87.36	90.71

TABLE 2 Discrimination Capability Improvement when the WLPC-SC Feature is Included within the Feature Set

Feature	Speech (%)	Music (%)	Global (%)
MFCC	92.85	91.64	92.24
All timbral features	92.81	90.87	91.84
All timbral features +	96.29	91.05	93.67
WLPC-SC

TABLE 3 Classification Accuracy Percentage. WLPC-SC vs. LPC-SC

Feature	Speech (%)	Music (%)	Global (%)
WLPC-SC	94.07	87.36	90.71
LPC-SC	87.84	73.66	80.75

TABLE 4 Classification Accuracy Percentage Results When Using: a) No Transformation, b) PCA, and c) LDA

Case	Speech (%)	Music (%)	Global (%)
No transformation	94.07	87.36	90.71
PCA	94.77	88.21	91.49
LDA	95.56	88.95	92.25

TABLE 5 Comparing RBF-SVM and 3-GMM for Speech/music Discrimination When LDA is used

Case	Speech (%)	Music (%)	Global (%)
3-GMM	93.47	86.95	90.21
RBF-SVM	95.56	88.95	92.25

TABLE 6 Classification Accuracy Percentage. SVM vs. SVM + Fuzzy System

Classifier	Speech (%)	Music (%)	Global (%)
RBF-SVM	95.56	88.95	92.25
RBF-SVM + Fuzzy system	97.46	98.85	98.15