The L3DAS project aims at providing new 3D audio datasets and software toolkits for the development of deep learning algorithms designed for 3D audio analysis. To this end, the project will focus on various immersive audio tasks, such as sound event detection and localization, sound source separation, speech recognition, speech enhancement, audio super-resolution, acoustic scene classification, acoustic echo cancellation and noise reduction, among others.

End-to-End Learning for 3D Acoustic Scene Analysis. We use our auditory system not only to listen and recognize sounds, but also to make spatial sense of the surrounding environment and navigate in it. The sense of spatial immersion in a sound field allows the user to clearly understand every sound surrounding in it, as well as any acoustic environment characterized by certain sounds.

The ELeSA project is mainly focused on the 3D acoustic scene analysis and understanding to detect, localize and classify sound sources and perfectly describe their nature. This goal also entails the audio quality enhancement of the signals recorded within the acoustic scene surrounding the user by means of 3D microphone arrays. 3D acoustic scene analysis can have a great impact in many applications including audio virtual reality, speech and sound recognition, safe and security. However, the same approach can be applied in many other fields of applications, from telecommunications to electronics to physics and manufacturing industry.