# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 1 Introduction  
Denoising is the process of extracting only the clean speech from a mixed sound of speech and noise. Figure 1 shows denoising of speech data. The main goal of denoising is to enhance the perceptual quality of speech and the robust speech recognition. Applications of denoising include cellular and teleconference communications affected by background and channel noise [1]. The denoising performance has a considerable impact on both the comprehensibility and the post-processing efficiency of the speech data. Therefore, various denoising methods have been studied [2]. However, as shown in Figure 1, we indicate that denoising, i.e., mitigating the noiseaffecting a speech signal, is a difficult process because the noise and speech coexist at the same time point. The previous denoising methods can be classified into the following three categories:  
1. Statistical feature-based methods: There have been previous studies that exclude the noise by the threshold value according to a specific statistical feature. The proposed representative criteria are nonnegative matrix factorization [3], Wiener filter [1,4,5], and wavelet transformation denoising [2,6]. Before AutoEncoder and deep learning-based methods were proposed, this approach had been widely used. 2. AutoEncoder-based methods: Denoising AutoEncoder (DAE) has been already used in image processing to extract the noise for the classification [7]. For denoising in speech signals, there have also been many previous studies to adopt the DAE to extract the noise from noisy speech data: DAE using the noisy speech as the input and the clean speech as the output [8], a new pre-training and fine tuning methods based on the DAE [9], weighted DAE capturing the relationship between the noisy and clean speech signals [10], time-domain convolutional DAE [11], and speaker-aware DAE [12]. This approach allows us to conduct unsupervised learning without manual labeling because the noises are automatically generated in the model. However, it has been known that the strength and accuracy of the model are lower than the deep learning methods, which are supervised learning based on the labeling of clean and noisy data sets. 3. Deep neural network (DNN)-based models: Deep learning-based denoising learns the difference between noisy speech data and clean speech data. Various deep learning models have been proposed: Wavenet-based denoising model [13], Fully-Convolutional Networks (FCNs) denoising model [14], Convolutional Neural Network (CNN) denoising model [15], Recurrent Neural Networks (RNNs) denoising model [16], and Convolutional-RNN (CRNN) denoising model [17]. This approach generally shows a high performance although it requires large-scale data sets with labeling and requires significant computing costs in the training process. One of recent advances in deep learning-based methods is the Wavenet-based algorithm, which is an end-to-end model developed by Google [13]. Wavenet has been used to produce sound waveforms within Tacotron [18], Google's voice synthesis model [13], which can identify speech features effectively. It has been shown that Wavenet-based denoising performs better than Wiener filter, which is one of the most widely used methods [4]. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets [19]. Furthermore, it becomes more important as the deep learning-based methods have been developed because they require significant costs while showing the high performance in general. Especially, deep learning model has been widely applied in various environments including not only high-performance servers equipped with GPUs but also low-performance embedded or IoT devices such as raspberry pi [20]. Therefore, reducing the computational cost in deep learning model is one of critical issues in practice because it allows us to provide real-time services based on the deep learning model even in limited environments [21]. Figure 1: Denoising of speech data.