### Related Work
It is possible to isolate and track the ENF in recordings to check whether there is phase inconsistency in the fluctuation, or even to find the exact time of the recording from the log files of the electronic networks [22; 23; 24; 25]. Other approaches investigate the acoustic environment of the recording, such as the Signal-to-Reverberation ratio of a room [26; 27]. The specifications of a recording device have also been proven to be traceable in research, providing an indicator of whether parts of an audio file were recorded with a different device [20; 28; 29; 30]. Dynamic Acoustic Environment Identification (AEI) may rely on statistical techniques that rely on reverberation and background noise variance in a recording [31]. Machine learning techniques are proven to be very useful for acoustic environment identification. Machine learning models do not rely on the definition of a set of rules for decision-making, but require a dataset of pre-annotated samples to train a classification model that can identify different classes, in this case, acoustic environments [31; 32; 33]. Another methodology for audio tampering detection investigates file encoding and compression characteristics. A huge number of highly configurable audio encodings are available that differ in terms of compression ratio, bitrate, use of low-pass filters, and more. A file that comes from audio splicing is very likely to contain segments encoded with different configurations, which can be traceable [34; 35; 36]. Most encoding schemes depend on psychoacoustic models that apply algorithms to discard redundant, inaudible frequencies. The Modified Discrete Cosine Transform (MDCT) coefficients can be investigated using statistical or machine learning methods to detect outliers in specific segments of the file [37]. Even when the file is reencoded in another format, there are often traces of the effect of previous compression algorithms [38; 39; 40; 41]. Media authentication can be supported during content production using container and watermarking techniques, such as hash-code generation and encryption, and MAC timestamp embedding. Recovery of the inserted hash code that was generated by algorithms, such as SHA-512, enables the detection of tampered points within an audio stream [42]. Similarly, embedding timestamp information in files can allow the identification of an audio excerpt with a different MAC timestamp that has been maliciously inserted [20]. Whether the aim is training machine learning models or evaluating proposed analysis methods, one crucial part of every audio authentication project is the formation of a dataset. This is a very complex procedure due to the task's peculiarities, and it often acts as a bottleneck for the robustness of such techniques. Not many datasets are available for experimentation. In [43], a dataset was recorded featuring different speakers, acoustic rooms, and recording devices. In [44] a dataset with different encodings was created through an automated process. In [45], existing recordings were edited to create a dataset. In [7], an automated process was proposed for the creation of a multi-purpose dataset using an initial set of source files provided by the user.