  We explore network sparsification strategies with the aim of compressing
neural speech enhancement (SE) down to an optimal configuration for a new
generation of low power microcontroller based neural accelerators (microNPU's).
We examine three unique sparsity structures: weight pruning, block pruning and
unit pruning; and discuss their benefits and drawbacks when applied to SE. We
focus on the interplay between computational throughput, memory footprint and
model quality. Our method supports all three structures above and jointly
learns integer quantized weights along with sparsity. Additionally, we
demonstrate offline magnitude based pruning of integer quantized models as a
performance baseline. Although efficient speech enhancement is an active area
of research, our work is the first to apply block pruning to SE and the first
to address SE model compression in the context of microNPU's. Using weight
pruning, we show that we are able to compress an already compact model's memory
footprint by a factor of 42x from 3.7MB to 87kB while only losing 0.1 dB SDR in
performance. We also show a computational speedup of 6.7x with a corresponding
SDR drop of only 0.59 dB SDR using block pruning.

Sound;Machine Learning;Audio and Speech Processing