## 2 Related Works
### Neural Network Models for Voice Emotion Analysis  
There is a Wiener filtering method that removes noise frequency bands while retaining the frequency bands of the sound to eliminate noise. However, it is challenging to remove all types of noise encountered in real-world scenarios using the Wiener filtering method alone. Consequently, recent studies have actively explored noise reduction using deep learning models. For instance, by training an autoencoder with noise, the model can extract noise features from noisy speech and then restore and remove noise from the original speech data [10].  
A study demonstrated that CNNs could be used to distinguish between speech and non-speech segments in human voice data, thereby eliminating unnecessary voice signals [11]. Although deep learning models have shown promising results in the field of noise reduction, they are noted for being vulnerable to untrained noises. A GAN-based generative model trained for noise reduction has been shown to perform better at removing untrained noise [12].  
The Speech Enhancement Generative Adversarial Network (SEGAN) was the first GAN-based deep learning model to demonstrate its effectiveness in the field of noise reduction [7]. The GAN-based deep learning model SEGAN outperformed the traditional STSA-MMSE (short-time spectral amplitude minimum mean square error) algorithm [13].  
In the field of Image-to-Image Translation, classical methods require training data to be paired, but collecting paired training data is often very costly. CycleGAN introduces the addition of a reverse generator to a conventional GAN, allowing for the use of unpaired training data [14]. Although the original CycleGAN was designed for image data transformation, it can also be applied to voice data by converting the voice into a spectrogram, allowing it to be processed as an image [15].  
In standard GANs, Cross Entropy loss is typically used as the discriminator's loss function. However, when updating weights with fake data that are far from real data, this can lead to the Vanishing Gradient problem. By using Least Square loss as the loss function, this problem can be mitigated, leading to more stable training [16].  
If the layers of a deep learning model are too deep, training can become difficult and performance may degrade. ResNet (Residual Neural Network) addresses this issue by using Skip Connections, which allows for the construction of very deep layers [17].  
General GANs can have unstable training. DCGAN stabilizes GAN training by incorporating convolution, batch normalization, and LeakyReLU. By using LeakyReLU instead of ReLU as the activation function in the discriminator, stronger gradients can be provided to the generator [18].  
For classifying an infant's emotions (discomfort, hunger, sleepiness), 32-dimensional FFT (Fast Fourier Transform) processed voice data can be used as training data, making PCA (Principal Component Analysis) feasible [19]. Infants under six months old use facial expressions, voice, heart rate, and body temperature as communication tools, with crying being the most common method. Therefore, analyzing an infant's state through voice is one of the critical techniques [20].  
When detecting an infant's crying using a deep learning model, visual processing of the voice with a CNN (Convolutional Neural Network) that uses a special asymmetric kernel can yield better results compared to standard CNN architecture. Using MACF (MelFrequency Cepstrum Coefficients) feature extraction and a BNN (Backpropagation Neural Network), an infant's crying can be classified into three categories: hunger, discomfort, and fatigue [21].