# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 3 Results and Discussions
### Performance Evaluation  
Performance evaluation of the VAD algorithm can be performed both subjectively and objectively. In subjective evaluation, a human listener evaluates for VAD errors, whereas, numerical computations are carried out for objective evaluation. However, subjective evaluation alone is insufficient to examine the VAD performance, because listening tests like ABC fail to consider the effects of false alarm [32; 34; 35]. Hence numerical computations through objective evaluation help in reporting the performance of the proposed VAD algorithm. VAD performance is calculated using (17) and (18),  
\[\text{HR}_{\text{ns}}=\frac{NS_{\text{ns},\ ns}}{NS_{\text{ns},ref}} \tag{17}\]  
and  
\[\text{HR}_{\text{s}}=\frac{NS_{\text{s},\ s}}{NS_{\text{s},ref}} \tag{18}\]  
where, \(\text{HR}_{\text{ns}}\) and \(\text{HR}_{\text{s}}\), non-speech frames and speech frames correctly detected among non-speech and speech frames, respectively. \(NS_{\text{ns}}\) and \(NS_{\text{s}}\), refer to the number of non-speech and speech frames in the whole database, respectively, while \(NS_{\text{ns},\text{ns}}\) and \(NS_{\text{s},s}\), refer to the number of frames classified correctly as non-speech and speech frames. The overall accuracy rate is given by (19),  
\[\text{Accuracy}=\frac{NS_{\text{ns},\text{ns}}+NS_{\text{s},s}}{NS_{\text{ns}, \ ref}+NS_{\text{s},\ ref}} \tag{19}\]The best performance is achieved when three parameters referred in the Equations (17)-(19) become maximum. Performance of the proposed FuzzyEn feature was evaluated by the SVM classifier (proposed) and was compared against \(k\)-NN classifier. In this work, 10-fold cross validation was used to ensure the reliability of the classifier. In 10-fold cross validation, the given feature vector was randomly divided into 90:10 split, where 90% of the features are used to train SVM model and 10% features as test data. The mean and standard deviation of the error rates obtained were compared against the two classifiers considered for the proposed FuzzyEn feature which is shown in Table 1. From Table 1, it is inferred that under low SNR levels the proposed FuzzyEn feature outperforms with minimal error rate for the various noisy conditions with the SVM classifier, except for car noise at 5 dB. All experiments were conducted with MATLAB version 7.11 on a 2.4 GHZ Intel(r) CoreTM i7 processor running Windows 10 with 8 GB main memory. Figure 4 shows the average computing time for the SVM classifier to classify the speech and non-speech frames under various noises. From the figure it is inferred that as the size of the number of frames increases the computing time increases. Figure 5a-c shows the mean of sensitivity, specificity, and F-measures of various noises such as airport, babble, car, and train noise for FuzzyEn based VAD using spectral subtraction. Similarly, Figure 6a-c shows the standard deviation of the sensitivity, specificity, and F-measures for various noises. For various SNR levels, ranging from \(-\)10 dB to 10 dB, the parameters were computed and the graph shows that the detection of speech and non-speech frames are good by the proposed method, especially under low SNR conditions, except for car interior noise which contains a complex audible instance, where the sensitivity decreases when compared with that of the other noises. The performance of the proposed FuzzyEn-SVM based VAD (will be referred to as FE-SVM based VAD hereafter) was compared against the standard VAD method, ITU G.729 Anne B [16], and with [13; 36]. Also the same was compared using \(k\)-NN classifier with and without spectral subtraction. The final VAD decisions were made and the performance metrics like accuracy, HRs, and HRns were computed for different noises and at five SNRs (\(-\)10, \(-\)5, 0, 5, and 10 dB). In Figure 7, the accuracy of the FE-SVM based VAD is compared against the various VAD algorithms for SNR levels ranging from \(-\)10 dB to 10 dB for airport noise, babble noise, car noise, and train noise.