AI and Voice Recognition are two technological fields that are converging in an increasingly evident way. This synergy is changing numerous sectors, from virtual assistance to autonomous driving.

The potential of Voice Recognition and the predictive capabilities of Artificial Intelligence are opening new frontiers in human-machine interaction.

After obtaining the national patent on “audio stream processing method for recognizing voices and/or background sounds and related system” on our audio technology, we continued to improve the “Polyphonic” platform.

In this article we will explore the main tools of this solution and the latest developments in the field of Audio Recognition.

Operation 1: Removing background noise

When you come into contact with audio files it is very rare that they are free of background noise.

Noise removal plays a crucial role in ensuring sound quality and clarity. Unwanted noise can arise from a variety of sources, such as clicks, hiss and crowding which can affect the intelligibility of the listener’s voice. Therefore, this operation is essential to obtain clean and professional recordings.

Background noise can be particularly noticeable in audio recordings taken outdoors or in noisy locations. Removing it helps improve the overall sound quality and makes the audio file suitable for more in-depth analysis.

However, it is important to note that noise removal must be done with care to avoid compromising the original sound quality. Some “cleaning” algorithms can cause unwanted artifacts or even affect the naturalness of the speaker’s voice. Therefore, it is essential to use tools that are effective without compromising the entire audio file.

Our “Remove Noise” tool allows you to obtain an optimal result in order to prepare the file for more precise analyses and generate correct audiometric graphs.

Operation 2: Speaker Characteristics

The analysis of speaker characteristics in an audio file plays a crucial role in carrying out complex investigations.

Identifying elements such as age, gender and language of the speaker enriches the understanding of the audio content. Furthermore, it provides valuable information in investigative operations, significantly reducing analysis times. Let’s see them in detail:

  • Age prediction. The first characteristic to consider is the age of the speaker. Age can significantly affect the pitch, rhythm and timbre of the voice. For example, younger people tend to have higher-pitched voices and a different speaking rate.
  • Gender prediction. The gender of the speaker is another crucial characteristic. Physiological differences between sexes are reflected in their voices, with men tending to have deeper voices and women tending to have higher-pitched voices. Identifying the speaker’s gender is a strategic operation for applications dealing with voice assistant systems or audiobooks.
  • Language prediction. The language spoken by the speaker can be crucial for understanding the audio content and for its correct processing. Each language has its own phonetic and prosodic characteristics that influence how it is pronounced and perceived.
  • Diarization – Number of speakers. It is essential to understand how many people are speaking in an audio file, so that you can analyze its characteristics individually. Furthermore, having a tool capable of dividing the various recordings into individual distinct files allows you to significantly reduce investigation times and the possibility of human error.

In conclusion, the task of AI is to be a valid support for experts and not to replace their work, increasing their ability to analyse and understand information. This hybrid approach makes the most of human skills and the efficiency of AI.

Operation 3: Comparation

One of the key operations within the “Polyphonic” platform concerns the comparison between multiple audios.

Audio Comparation is a fundamental process in the fields of music production, sound engineering and audio quality in general. It consists of comparing two or more audio tracks in order to evaluate their differences and similarities.

Furthermore, this operation can be used to evaluate the reproduction fidelity of audio devices and speaker systems. Professionals compare the reproduction of a sound on different devices in order to identify any differences in the sound performance, such as tonal coloration, distortion or loss of quality. This helps ensure that sound is reproduced accurately across a wide range of devices and listening environments.

Finally, Audio Comparation can also be used in forensic analysis and audio security. Experts compare audio recordings to identify manipulation, unauthorized editing or attempted falsification. This process is critical in legal and investigative contexts where the veracity and integrity of audio evidence is crucial.


We at Team Pragma Etimos continue to study and develop innovative and functional solutions in the field of Audio Recognition.

The integration of our audio technology into modern solutions used for sound recognition is proving successful in terms of performance and accuracy of results.


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