Description: Aural Models are a type of multimodal model that focuses on analysis and prediction based on audio data. These models integrate various signal processing techniques and machine learning to extract meaningful information from the acoustic features of recordings. Their approach allows not only the interpretation of sounds but also the identification of patterns and the classification of sound events. Often, these models use deep neural networks, which can learn complex representations of audio data, facilitating tasks such as voice recognition, emotion detection in speech, and music genre classification. The ability of Aural Models to combine information from different sources, such as text and audio, makes them powerful tools in the fields of artificial intelligence and natural language processing. Their relevance has increased in recent years, driven by the growth of applications that require a deeper understanding of human communication and the sound environment. In summary, Aural Models represent an innovative intersection between audio technology and machine learning, offering new possibilities for human-computer interaction and sound data analysis.
