# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 3 Proposed Methodology
### Proposed Method  
The key aim of the study is to extract features that enhance the accuracy for detection and classification of voice pathology and to investigate the impact on the detection and classification processes of different frequency regions (bands). Before feeding to a convolutionary neural network (CNN), the voice signals are processed. To use existing stable CNN models, we use a transfer-learning platform. The paper explores, in particular, the ResNet34 models. The block diagram of the proposed solution is shown in Figure 1.  
Figure 1 demonstrates the proposed deep learning approach to the detection of voice pathology. The system is fed a patient's voice, and the output determines whether the patient's voice is normal or pathological. The signal of the voice is 1 s. If an input reaches 1 s, then a signal of 1 s is cut from the centre. The signal is split in 40 ms frames, of which the gap is 20 ms. The 40 ms frame duration is a well-equilibrated pitch capture and voice breaks smoothing option. If this is very long, then the voice breaks or some sounds cause the vocal folds to be irregularly opened and closed. The continuation effect and pitch duration are lost if the frame length is short. The framed signal is transformed by a fast Fourier transform to a frequency-domain signal. We get a spectrogram after concatenating all frequency-domains of the frames. The spectrogram could be viewed as an image. The spectrogram includes a minimum of 20 filters for the band pass. The filters are based on the octave. In the area of voice pathology detection, the octave scale typically functions better than the Mel scale [34]. Time derivatives of the first and second order for the octave spectrum output are used. After this method, we get three image-like patterns: the octave and its derivatives of first and second order. The input of the CNN models is made up of three image patterns. We tested ResNet34 in the proposed method. In this paper, for several reasons the transfer learning strategy for the CNN training is applied: (i) to overcome the lack of adequate voice pathologic attributes, in particular voice diseases derived from patients with reported infections, (ii) reducing the learning duration needed to acquire the final learned typical, and (iii) increasing the classification precision of the voice pathology identification. The technique of transfer learning is intended to boost neural network output in realistic applications bypassing learning from another task [45]. For example, the training of a CNN to classify the case into two groups (e.g., pathological or healthy) may help to classify cases of different disease types. In this case, we used an effective ResNet34 pertained model. Residual Network (ResNet) is one of the highest-profile CNNs and the recipient of the 2015 ILSVRC ImageNet classification award [46]. ResNet is much like the other CNNs, which are sequentially packed with convolutionary, pooling, activation maps and fully interconnected layers. The only big difference between ResNet and other CNNs is the connection identity from the input layer to the end of the residual block (as shown in Figure 2b). The architecture of ResNet34 begins with a convolutionary operation and max-pooling of the use of size kernels (5*5) pixels and (2*2) pixels, respectively. Thereafter, four stages with a different number with residual blocks are introduced, using size kernels (2*2) pixels to perform the convolutionary  
Figure 1: Voice signal processing in the proposed system. operation. When one passes from one point to the next, the depth of the channel is doubled, and the size of the input sample is halved. In this study the ResNet34 has an average pooling layer with two neurons (for example, positive pathological and normal case as healthy) followed by a completely connected layer. Table 1 illustrates the main details of the ResNet34 architecture.