5 citations · 5 across the 2 of their papers we have counts for
5 papers
Transformer-based Neural Beamforming for Real-Time Speech Enhancement on Smart Low-Power Hearable Devices
Luca Bompani, Marco Fariselli, Giovanni Oltrecolli +1
Accurate, efficient, and low-latency spatial beamforming is a key component in emerging smart hearable devices, enhancing speech while suppressing noise and interference. However,…
Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System
Lorenzo Lamberti, Manuele Rusci, Marco Fariselli +2
In this paper, we present the first (to the best of our knowledge) demonstration of a low-power MCU-based edge device for Automatic License Plate Recognition (ALPR). The design lev…
Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors
Manuele Rusci, Francesco Paci, Marco Fariselli +2
This paper proposes a self-learning method to incrementally train (fine-tune) a personalized Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio sensor…
Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization
Manuele Rusci, Marco Fariselli, Martin Croome +2
This paper presents an optimized methodology to design and deploy Speech Enhancement (SE) algorithms based on Recurrent Neural Networks (RNNs) on a state-of-the-art MicroController…
Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers
Manuele Rusci, Marco Fariselli, Alessandro Capotondi +1
The severe on-chip memory limitations are currently preventing the deployment of the most accurate Deep Neural Network (DNN) models on tiny MicroController Units (MCUs), even if le…