## 1 Introduction  
The proliferation of technologies and platforms for sharing information has made the increase in information in recent years exponential. Within this large amount of information, specific needs arise related to very diverse aspects such as authentication and integrity verification [1], content security [2], application of copyright [3], covert communication [4], or identification of multimedia contents [5], among others. Some of these applications fall within what is known as digital forensics. Digital forensics is a discipline whose main objective is the application of computer tools in legal proceeding or an official investigation of some kind. It is used by experts to analyze, preserve and produce storage data for both volatile and non-volatile media, contributing to the veracity of digital evidence that is legally provided [6; 7; 8]. One of the fields of this discipline is audio forensics, where the aim is to acquire, analyze, or evaluate audio recordings which may be presented as admissible evidence in some official venue [9; 10; 11]. Some specific tasks normally performed in audio forensics include authentication and integrity verification and the identification of multimedia contents. In the case of authentication and integrity verification applications, the aim is to ensure that the information (e.g., digital evidence) was not inadvertently or deliberately altered prior to examination,i.e., what is being demonstrated is that the content has not changed [9; 12]. On the other hand, the identification of multimedia contents can be useful when there is only partial access to the information; this is the case of song identification systems (from a few seconds), or also of digital forensic systems, when the data have suffered fragmentation or partial destruction [13]. It is important to note here that the integrity check seeks to demonstrate that a content has not changed, while the multimedia content identification seeks to determine if the fragment is part of a content that has previously been identified as incriminating. In any case, how these challenges are addressed depends on the characteristics and availability of information on the content to be evaluated. This is usually achieved through the calculation of a numerical value based on input data, i.e., the use of cryptographic hash functions [14]. However, due to the high sensitivity of these functions to minor changes in the input, this solution is not practical when exact copies of the content to be evaluated are not available [5]. This means that, if you only have partial access to content, or even if the content may have changed (e.g., in the file format), the solution is not appropriate. Similarly, alternatives such as encryption methods, cyclical redundancy check, or even meta-data use may fail when digital content is modified such as via file format conversion, meta-data removal, or analog/digital and digital/analog conversions [5; 15]. In this type of situation, the methods that can be used must be robust enough to tolerate certain modifications, or even allow identification using only part of the contents [16]. These methods mainly include watermarking and robust hash functions. The first option is an invasive technique, consisting of the imperceptible insertion of a specific code over the content [17]. This option is valid when the content is susceptible to modification, but in some cases it may present drawbacks such as scalability or the need to previously mark all content to be evaluated [5]. In the case of robust hash functions, also known as perceptual hash functions or audio fingerprints, these are characterized as non-invasive. In this case, a static or at least almost insensitive behavior in the hash code with respect to permissible transformations in such content is desired [15]. In this context, the approach of robust hash functions for the identification of audio contents has aroused recent interest due to its multiple applications.