L3DAS21: Machine Learning for 3D Audio Signal Processing

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L3DAS21: Machine Learning for 3D Audio Signal Processing


3D audio is gaining increasing interest in the machine learning community in recent years. The field of application is incredibly wide and ranges from virtual and real conferencing to game development, music production, autonomous driving, surveillance and many more. In this context, Ambisonics prevails among other 3D audio formats for its simplicity, effectiveness and flexibility. Ambisonic recordings permit to obtain an impressive performance in many machine learning-based tasks, usually bringing out a significant improvement over the mono and stereo formats. Tasks like Sound Source Localization, Speech and Emotion Recognition, Sound Source Separation Separation, Speech Enhancement and Denoising, Acoustic Echo Cancellation, among others, benefit from tridimensional representations of sound field, thus leading to higher accuracy and perceived quality.

The L3DAS project (Learning 3D Audio Sources) aims at encouraging and fostering research on the afore-mentioned topics. In particular, the L3DAS21 Challenge focuses on 2 tasks: 3D Speech Enhancement and 3D Sound Event Localization and Detection, both relying on Ambisonics recordings. First, provide the training and development sets, alongside with a supporting python-based API to facilitate the data download and pre-processing. We also supply baseline results for both tasks, obtained using state-of-the art deep learning architectures. In a second step, we will release the test sets without truth labels.

Participants must submit the results obtained for the latter. In the end, the final ranking of the challenge will be presented at the IEEE Workshop on MLSP and released on the challenge webpage. Full Challenge Document (pdf).

Technical Committee: Machine Learning for Signal Processing


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