# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 1 Introduction
In this paper, we deal with the deep learning-based model for denoising and focus on improving its denoising speed while maintaining the accuracy of denoising. The importance of efficient denoising is supported by our experimental results, which show that the denoising time of Wavenet-based denoising (e.g., 3867 s) is much larger than the original signal lengths (e.g., 2072 s) in our experimental setting and it is significantly reduced by the proposed strategy (See Section 5). In this paper, we propose a preprocessing strategy for denoising of speech data based on the speech segment detection. Figure 2 shows the concept of the proposed preprocessing strategy for denoising. The basic idea of the proposed method is using the speech segment detection so as to exclude non-speech segments before denoising. The speech segmentation detection can exclude non-speech segments with a negligible cost, which will be removed in denoising process with a much higher cost, while maintaining its accuracy of denoising. As shown in Figure 2, the proposed preprocessing strategy consists of the following four steps:  
1. We speculate the target environment using samples of noisy and clean data files to figure out the characteristics of the environment. To define the environment, we use different levels of signal-to-noise ratio (SNR) and multiple evaluation criteria, which affect the results of preprocessing significantly. That is, the effects of the speech segment detection methods are quite varied according to the level of SNR (See Section 4). In addition, we need to determine a preferred evaluation criterion. Specifically, in some environments, we should not allow to exclude any small speech segments even if many non-speech segments are not excluded (i.e., recall takes the precedence over precision); in other environments, we can improve the overall effects of denoising by allowing to exclude some negligible speeches (i.e., precision over recall). 2. We enumerate the speech segment detection methods by combining filtering and unsupervised methods that have been used for the voice activity detection [22] and conduct their performance evaluation to select the most effective method for a target environment. Here, we note that the purpose of the speech segment detection methods is effectively excluding the non-speech segments as preprocessing of denoising, not improving the performance of the speech segment detection itself. As a result, we investigate simple and efficient speech segment detection methods that can work effectively with the denoising method. 3. We apply the speech segment detection method with the best setting by each method into noisy data files. In this step, the non-speech segments, which will be removed with a significant cost in deep learning-based denoising process, are efficiently excluded, while the overall accuracy is maintained. 4. We apply the Wavenet-based denoising model [13], which is one of recent advanced denoising methods based on deep neural networks, to only the speech segments. Through extensive experiments, we evaluate the performance of the proposed strategy by each speech segment detection method where the best setting in the previous step is used and compare them with the original Wavenet-based denoising model in terms of the speed and accuracy of denoising. We summarize the contributions of the paper as follows:  
1. We devise a framework to choose the best preprocessing method for denoising based on the speech segment detection for a target environment. For this, we speculate the environments for denoising using different levels of SNR and multiple evaluation metrics. As shown in Figure 2, the framework finds the best speech segment detection method tailored to a target environment according to the performance evaluation of speech segment detection methods. 2. We investigate the accuracy of the speech segment detection methods extensively. We conduct the performance evaluation of five speech segment detection methods with different levels of SNRs and multiple evaluation metrics.