Audiovisual data is of vital importance for the entertainment industry (digital media, television, radio, podcasts, video games, music, etc.) in which its combination with telecommunications has radically changed our lives, especially in the way we which we interact with said data (intelligent assistants such as Siri, Alexa, Google Assistant, etc).
In addition, these data are increasingly relevant in areas such as medicine where the increasing sophistication of sensing devices and even wearable devices such as smart watches, virtual and augmented reality glasses, etc., generate more and more types of data with immense potential for transforming society and creating new markets.
But the key technology that has become essential to transform this large amount of audiovisual data into useful information and knowledge is artificial intelligence or machine learning, including neural networks and deep learning.
Therefore, the objective of this subject is to provide students with theoretical and methodological knowledge on algorithms and methods for the analysis of audiovisual information, including retrieval and indexing of multimedia information for navigation and search, user profiling, opinion mining and positioning, personalization of recommendations, etc.
In addition, an eminently practical point of view will be adopted, providing the tools to put theoretical knowledge into practice in the laboratory, so that students end up being able to develop an audiovisual data analysis project based on machine learning.
Ultimately, this will allow connections to be made with the myriad of applications the business products and services they support (for example, various Google services, platforms like Twitter, Instagram, TikTok, Spotify, Netflix, YouTube, Twitch, Shazam, and more).
1. TRANSVERSAL/GENERIC COMPETENCES:
1.1. Personal work capacity.
1.2. Capacity for analysis and synthesis.
1.3. Ability to apply theoretical concepts in practical cases.
1.4. Skills related to group work and collaboration with other colleagues.
1.5. Skills related to making oral and written presentations.
2. SPECIFIC OBJECTIVES:
2.1. To understand the fundamentals of audio-visual data analysis and its applications.
2.2. To understand the basic methods of representation and description of speech, audio, image and video.
2.3. To understand the methods and technologies used for classification, detection, indexing, recovery, filtering, personalization or recognition of voice, audio, image or video.
2.4. Ability to design and implement the above methods and technologies in practical problems of automatic voice, audio, image and video analysis.