# Source Microphone Identification Using Swin Transformer
## 2 Related Work
[30] present a method for identifying microphone devices in noisy environments using speaker-independent features. The proposed approach involves extracting features from the audio signal, such as the cepstral mean and variance, and then using machine-learning algorithms to classify the microphones. [PERSON] et al. [5] present a method for classifying digital audio recordings based on the microphone and recording environment used to capture them. The authors use the CRNN model to train a model that can accurately classify audio recordings based on features such as spectrogram and noise level. [PERSON] et al. [31] present a classification model for recognizing the type of microphone being used. The model uses a multi-layer perceptron network to determine the microphone type based on characteristics such as frequency response and noise level. The paper of [PERSON] et al. [32] presents a method for identifying the source cell phone of a recorded audio signal in the presence of additive noise using the constant-Q transform (CQT) domain. The proposed method first applies the CQT to the recorded signal and then extracts a set of features, including the mean and standard deviation of the CQT magnitude coefficients. These features are then used to train a machine-learning classifier, such as a support vector machine (SVM), to recognize the source cell phone. [PERSON] et al. [33] present a method for identifying the device used to record audio based on the unique characteristics of the background noise present in the recording. [PERSON] et al. [34] propose a method that involves analyzing statistical properties of the audio, such as frequency content and noise levels, and comparing them to known characteristics of the microphone. [PERSON] et al. [35] identify microphones using spectral entropy and a convolutional neural network (CNN). The authors extract spectral entropy features from audio recordings made with different microphones and use a CNN to classify the recordings based on their spectral entropy features. [PERSON] et al. [36] propose a method for identifyingrecording devices using deep learning. The method uses CNN to extract features from audio recordings and a support vector machine (SVM) to classify the recording devices based on these features. The authors [37] present a method for identifying smartphones based on their microphone characteristics when recording audio in different environments. The authors collected data from various smartphones in different environments and used a linear discriminant classifier (LDA) and a CNN to analyze the data. [PERSON] et al. [38] propose a sub-band-aware CNN model for cell phone recognition. The model uses sub-band information to improve the accuracy of cell phone recognition by capturing frequency-specific features of the audio signal. [PERSON] et al. [39] collect a dataset of microphone recordings and use it to train a CNN to classify microphone brands. The results indicate that CNNs can be effectively used for microphone identification. The paper [40] proposes a method for identifying smartphones using the built-in microphone and a CNN. The authors collected a dataset of audio recordings from different smartphone models and used it to train the neural network. Previous research has shown varying levels of accuracy for proposed systems in digital audio forensics, with many of these studies relying on local databases that cannot be generalized for several reasons. For instance, some of these methods utilize databases with significant classes variations, making the classification task relatively simple. Conversely, other databases are recorded using pre-set parameters that do not reflect the actual conditions of audio recordings used in digital forensics. Furthermore, some databases incorporate multiple microphones connected to different recording devices, making it unclear whether the result was to classify the microphones or the recording devices.