# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 3 Denoising Based on the Speech Segment Detection
### Filtering with Fuzzy Clustering  
Now, we investigate methods that combine fuzzy clustering with energy-based or entropy-based filtering methods. Specifically, we first extract the speech segments using the filtering method, and then, conduct fuzzy clustering on only the segments that are excluded by the filtering method. We define two kinds of methods in this approach: (1) energy-based filtering with fuzzy clustering and (2) entropy-based filtering with fuzzy clustering. Similar to fuzzy clustering, we use a threshold probability for finding the best parameter setting.  
Figure 15 shows the accuracy variation of energy-based filtering with fuzzy clustering as threshold is varied from 10% to 100%. In the result, we note that the recall decreases significantly, but precision increases slightly as the threshold increases. When the threshold is 50%, the precision and the F1 score are the highest. Figure 16 illustrates the results of energy-based filtering with fuzzy clustering. Here, we note that this method recovers some segments that have been excluded by the energy-based filtering method.  
Figure 14: The result of fuzzy clustering.  
Figure 12: The result of entropy-based filtering.  
Figure 13: The accuracy variation of fuzzy clustering as the threshold is varied.  
Figure 17 shows the accuracy variation of entropy-based filtering with fuzzy clustering as the threshold is varied from 10% to 100%. The overall trend is quite similar to energy-based filtering with fuzzy clustering. The F1 score is the highest when the threshold is 80%. Figure 18 illustrates the result of entropy-based filtering with fuzzy clustering. Similar to energy-based filtering with fuzzy clustering, this method also recovers the speech segments that have been excluded by entropy-based filtering.  
Figure 16: The result of energy-based filtering with fuzzy clustering.  
Figure 17: The accuracy variation of entropy-based filtering with fuzzy clustering as the threshold is varied.  
Figure 15: The accuracy variation of energy-based filtering with fuzzy clustering as the threshold is varied.  
We analyze the results of the filtering method, fuzzy clustering, and filtering with fuzzy clustering. Here, we show the result of entropy-based filtering, fuzzy clustering, and entropy-based filtering with fuzzy clustering. Figure 19 represents the distribution of data sets according to each method where the x-axis represents the entropy and y-axis the energy by the audio signal. Figure 19a represents the answer classification of speech and non-speech segments. Figure 19b represents the classification after entropy-based filtering. Here, we indicate that this simple filtering method can effectively classify speech and non-speech signals, but some speech signals are classified as non-speech signals. Figure 19c represents the classification of fuzzy clustering for all the audio signals. Figure 19d represents the classification of fuzzy clustering only for non-speech signals that have been excluded by entropy-based filtering. Here, we note that, fuzzy clustering can recover some signals that have been excluded by entropy-based filtering. Figure 19e represents the final result of entropy-based filtering with fuzzy clustering.  
Figure 19: Analysis of the accuracy improvement of entropy-based filtering with fuzzy clustering.  
Figure 18: The result of entropy-based filtering with fuzzy clustering.