most citedCluster-based pruning techniques for audio data

1 citations · 1 across the 2 of their papers we have counts for

collaborators
Showing eess.ASShow all

2 papers · 1 filter

eess.AS2023★ 1 cited

Cluster-based pruning techniques for audio data

Boris Bergsma, Marta Brzezinska, Oleg V. Yazyev +1

Deep learning models have become widely adopted in various domains, but their performance heavily relies on a vast amount of data. Datasets often contain a large number of irreleva…

eess.AS2021

PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition

Boris Bergsma, Minhao Yang, Milos Cernak

At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation ha…