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