Weakly-Supervised Multi-Task Learning for Audio-Visual Speaker Verification

In this paper, we present a methodology for achieving robust multimodal person representations optimized for open-set audio-visual speaker verification. Distance Metric Learning (DML) approaches have typically dominated this problem space, owing to strong performance on new and unseen classes. In our work, we explored multitask learning techniques to further boost performance of the DML approach and show that an auxiliary task with weak labels can increase the compactness of the learned speaker representation. We also extend the Generalized end-to-end loss (GE2E) to multimodal inputs and demonstrate that it can achieve competitive performance in an audio-visual space. Finally, we introduce a non-synchronous audio-visual sampling random strategy during training time that has shown to improve generalization. Our network achieves state of the art performance for speaker verification, reporting 0.244%, 0.252%, 0.441% Equal Error Rate (EER) on the three official trial lists of VoxCeleb1-O/E/H, which is to our knowledge, the best published results on VoxCeleb1-E and VoxCeleb1-H

Sub-mW Receiver Front-end for Bluetooth Low Energy in 130nm Technology

Bluetooth Low Energy (BLE) is the new operative mode introduced in the fourth release of the Bluetooth wireless technology standard. It is primarily designed for portable devices powered by coin cell batteries, including sensors and wearable biomedical devices. Under this standard, minimal power consumption is essential for the long-term functionality that these devices require. In this thesis, a sub-mW BLE receiver front-end operating under a 0.8 V supply is presented. Low power consumption is obtained sharing the same bias current among all the RF front-end building blocks and the first stage of the base-band. With an NF of 15.8 dB, an IIP3 of -17dBm at the maximum gain and an image rejection above 30 dB the receiver is fully compliant with BLE specifications with a minimum sensitivity of -84.2 dBm.M.A.S