Aurian Quelennec

• Master of Engineering
• PhD Student at Télécom Paris

Recently, self-supervised learning methods based on masked latent prediction have proven to encode input data into powerful representations. However, during training, the learned latent space can be further transformed to extract higher-level information that could be more suited for downstream classification tasks. Therefore, we propose a new meth...

Current state-of-the-art audio analysis systems rely on pre-trained embedding models, often used off-the-shelf as (frozen) feature extractors. Choosing the best one for a set of tasks is the subject of many recent publications. However, one aspect often overlooked in these works is the influence of the duration of audio input considered to extract...

A perceptual threshold related to spatial resolution of the human voice directivity was determined through a listening test of similarity (MUSHRA). Directivity data of an artificial talking head measured at high spatial resolution (spherical harmonics order 35) was the input of a room acoustics simulation software (RAVEN) to build sound stimuli in...