provide an information concealment technique in which a representative mark of the author is inserted into the file that is intended to be shared, seeking to protect the copyright [6; 7]. On the other hand, a hash function (also known as digest function) allows producing a distinctive summary from an entry based on its content, generating a fixed length text string (hash code). Since hash functions are highly sensitive to changes in content, these types of functions are quite useful to demonstrate the integrity of audio signals. Recent proposals include cloud-based solutions for crime reporting [8], cloud based integrity verification by means of hash functions [9], integrity verification using blockchain [10], and evidence retrieval based on semantic methodologies [11]. Even though hash functions applicable to any type of content have been designed, in some cases, it may be advisable to design specific functions according to the content type; for multimedia files, hash functions can be designed to be applied in audio, image or video signals [12]. In addition, it is necessary to consider that the cryptographic hash functions designed for forensic files must comply with the following fundamental properties: the first property indicates that it should not be possible to recover the original signal from the hash code (preimage resistance); the second and third properties define collision resistance, and they establish that it must be computationally infeasible for a given input, to find a different input with the same hash code. In the same way, it must be difficult to find two different files that generate the same hash code [13]. Currently, there are several mechanisms that seek to attack hash functions such as brute force attack and cryptanalytical attack [14; 15; 16]; therefore, the number of output bits in the hash code should be considered when designing the system. It is also necessary to clarify that collisions will always exist since the mapping process is not one to one [17]. Moreover, it has been shown that certain hash functions widely used in the forensic context, such as MD5 and SHA-1, may be susceptible to collisions [18; 19]. For the audio case, some hash functions are oriented to be perceptual, which are characterized by tolerating changes in the signal that do not significantly alter its content, consequently the generated hash code does not undergo modifications. Some examples of perceptual hash functions apply the non-negative matrix factorization (NNMF) of Mel-frequency Cepstral coefficients (MFCC) and then, through Principal Component Analysis (PCA), obtain a hash code of the audio signal [20]. It is possible to find solutions that use balanced multiwavelets (BMW) using five levels of decomposition and division of coefficients in the sub-bands [21]; it is also possible to find solutions based on MDCT (Modified Discrete Cosine Transform) that allow the extraction of characteristics of the signal [22]. Another variant is based on the ordering of the spectral coefficients obtained from the FFT calculation (Fast Fourier Transform) of the voice signal and a Gaussian noise signal generated from the statistics of the original signal [23]. Regarding the methods to calculate the hash code of an audio signal, the existing solutions are aimed at generating the summary of the signal after the recording (for example, once the evidence has been collected). An alternative to this approach is to generate the hash code of the audio signal immediately after its recording, as well as to store the code in a secure manner. In this sense, this paper presents the design and development of a solution capable of calculating a hash code; it is designed for speech signals without compression, and it can be used for forensic purposes. The solution operates in real time (at the time of the events) and allows one to guarantee the integrity of audio signals recorded on a mobile device with Android operating system and storage of the hash code in Microsoft Azure.