# Source Microphone Identification Using Swin Transformer
## 2 Related Work  
Microphone identification for audio forensics is a rapidly growing field with a wide range of research and applications. The goal of microphone identification is to define the type of microphone used to record a specific audio clip, which can be used in forensic investigations to determine the source of a recording or to authenticate a recording. The researchers used different feature extraction methods and systems to verify the microphone's authenticity. The electric network frequency (ENF) signal results from electromagnetic interference from power lines, which manifests as an acoustic hum that is susceptible to being picked up by microphones close to power mains [15]. The ENF has been utilized in digital recording authentication [16], as evidenced by numerous early and current studies focusing on ENF-based techniques [9]. Nonetheless, there are situations where such techniques cannot be applied, such as audio devices powered by batteries in areas away from the power grid [17]. [PERSON] et al. [18] propose a method for classifying microphones using Fourier coefficients, and the results suggest that Fourier coefficients can be useful features for microphone classification. [PERSON] et al. [19] present a method for identifying audio sources using a residual network. The network is trained on a dataset of mixed audio sources and is able to separate and identify the individual sources. A new approach to improve the performance of identifying the source cell phone is demonstrated in [20]. The method employs a spatiotemporal representation learning approach. [PERSON] et al. [21] evaluate various entropy measures for identifying microphones based on their audio recordings. The study found that the Renyi entropy measure performs the best in terms of accuracy, followed by the Shannon entropy measure. The authors also find that using a combination of entropy measures improves the overall performance of microphone identification. [PERSON] et al. [22] present a method for determining the source of an audio recording using the \"band energy difference\" (BED) technique. The BED method compares the energy levels of different frequency bands in the recording to a database of known sources and uses the differences in energy levels to identify the most-likely source of the recording. The method in [23] uses deep-representation learning to extract features from the speech recordings and then applies spectral clustering to group the users into clusters based on those features. [PERSON] [24] uses wavelet-based features to extract characteristics of the speech signal and then uses a machine-learning algorithm to classify the signal based on these features. In [25], a model for verifying a person's identity using speech recordings taken from their cell phone is presented. The technique involves sparse representation to compare the recording with a pre-existing reference sample to determine whether the speakers are who they claim to be. [PERSON] et al. 's [26] method involves extracting features from speech recordings using a deep-Gaussian-supervector model and then using spectral clustering to group similar speakers together. [PERSON] et al. 's [27] method uses a single-Gaussian-mixture model to represent the audio from a single microphone and then compares the model with a database of known microphone models to identify the source microphone. [PERSON] et al. [28] introduce a method for recognizing the source microphone using a kernel-based projection technique to map the audio data into a high-dimensional feature space, where a classifier can be trained to distinguish between different microphones. [PERSON] et al. [29] propose a method for identifying the source cell phone used to make a speech recording by using sparse representation and the KISS metric. The method involves first extracting features from the speech recording, then representing these features using sparse coding, and finally comparing the sparse representations using the KISS metric to identify the source cell phone. [PERSON] et al.