18 citations · 36 across the 7 of their papers we have counts for
7 papers
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons
Mattias Nilsson, Ton Juny Pina, Lyes Khacef +3
With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive…
Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions
Mattias Nilsson, Olov Schelén, Anders Lindgren +4
Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the r…
On the Relevance of Bandwidth Extension for Speaker Verification
Marcos Faundez-Zanuy, Mattias Nilsson, W. Bastiaan Kleijn
In this paper, we consider the effect of a bandwidth extension of narrow-band speech signals (0.3-3.4 kHz) to 0.3-8 kHz on speaker verification. Using covariance matrix based verif…
On the relevance of bandwidth extension for speaker identification
Marcos Faundez-Zanuy, Mattias Nilsson, W. Bastiaan Kleijn
In this paper we discuss the relevance of bandwidth extension for speaker identification tasks. Mainly we want to study if it is possible to recognize voices that have been bandwit…
Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses
Mattias Nilsson, Foteini Liwicki, Fredrik Sandin
Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep lea…
Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic Processor
Mattias Nilsson, Foteini Liwicki, Fredrik Sandin
Spiking neurons can perform spatiotemporal feature detection by nonlinear synaptic and dendritic integration of presynaptic spike patterns. Multicompartment models of non-linear de…
Synaptic Delays for Temporal Feature Detection in Dynamic Neuromorphic Processors
Fredrik Sandin, Mattias Nilsson
Spiking neural networks implemented in dynamic neuromorphic processors are well suited for spatiotemporal feature detection and learning, for example in ultra low-power embedded in…