5 citations · 9 across the 5 of their papers we have counts for
7 papers
Does per-frame early exit pay? A compute-matched study of dynamic depth for on-device speech enhancement
Clément Laroche, Riccardo Miccini
Deep learning-based speech enhancement is increasingly deployed on-device in hearing aids, headsets, and earbuds. Most of these devices, however, can only accelerate static int8 gr…
Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement
Clément Laroche, Rasmus Kongsgaard Olsson
Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontroller NPUs can accelerate neural i…
From Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks
Riccardo Miccini, Clément Laroche, Tobias Piechowiak +2
Speech Enhancement (SE) in audio devices is often supported by auxiliary modules for Voice Activity Detection (VAD), SNR estimation, or Acoustic Scene Classification to ensure robu…
Adaptive Slimming for Scalable and Efficient Speech Enhancement
Riccardo Miccini, Minje Kim, Clément Laroche +2
Speech enhancement (SE) enables robust speech recognition, real-time communication, hearing aids, and other applications where speech quality is crucial. However, deploying such sy…
Scalable Speech Enhancement with Dynamic Channel Pruning
Riccardo Miccini, Clement Laroche, Tobias Piechowiak +1
Speech Enhancement (SE) is essential for improving productivity in remote collaborative environments. Although deep learning models are highly effective at SE, their computational…
Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps
Mattias Nilsson, Riccardo Miccini, Clément Laroche +2
As speech processing systems in mobile and edge devices become more commonplace, the demand for unintrusive speech quality monitoring increases. Deep learning methods provide high-…
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms
Riccardo Miccini, Alessandro Cerioli, Clément Laroche +3
Despite the recent advances in model compression techniques for deep neural networks, deploying such models on ultra-low-power embedded devices still proves challenging. In particu…