4 citations · 8 across the 5 of their papers we have counts for
5 papers
Woosh: A Sound Effects Foundation Model
Gaëtan Hadjeres, Marc Ferras, Khaled Koutini +7
The audio research community depends on open generative models as foundational tools for building novel approaches and establishing baselines. In this report, we present Woosh, Son…
Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks
Alexandre Bittar, Philip N. Garner
Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech rec…
Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder
Alexandre Bittar, Paul Dixon, Mohammad Samragh +2
Using a vision-inspired keyword spotting framework, we propose an architecture with input-dependent dynamic depth capable of processing streaming audio. Specifically, we extend a c…
Surrogate Gradient Spiking Neural Networks as Encoders for Large Vocabulary Continuous Speech Recognition
Alexandre Bittar, Philip N. Garner
Compared to conventional artificial neurons that produce dense and real-valued responses, biologically-inspired spiking neurons transmit sparse and binary information, which can al…
Bayesian Recurrent Units and the Forward-Backward Algorithm
Alexandre Bittar, Philip N. Garner
Using Bayes's theorem, we derive a unit-wise recurrence as well as a backward recursion similar to the forward-backward algorithm. The resulting Bayesian recurrent units can be int…