activity
20172022
most citedNow Playing: Continuous low-power music recognition

21 citations · 47 across the 4 of their papers we have counts for

collaborators

6 papers

cs.SD20222 cited

SpeechPainter: Text-conditioned Speech Inpainting

Zalán Borsos, Matt Sharifi, Marco Tagliasacchi

We propose SpeechPainter, a model for filling in gaps of up to one second in speech samples by leveraging an auxiliary textual input. We demonstrate that the model performs speech…

eess.AS20207 cited

Training Keyword Spotters with Limited and Synthesized Speech Data

James Lin, Kevin Kilgour, Dominik Roblek +1

With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tas…

eess.AS2019

SPICE: Self-supervised Pitch Estimation

Beat Gfeller, Christian Frank, Dominik Roblek +3

We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations…

eess.AS201917 cited

Fréchet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms

Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek +1

We propose the Fréchet Audio Distance (FAD), a novel, reference-free evaluation metric for music enhancement algorithms. We demonstrate how typical evaluation metrics for speech en…

eess.AS2018

Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition

David B. Ramsay, Kevin Kilgour, Dominik Roblek +1

Low power digital signal processors (DSPs) typically have a very limited amount of memory in which to cache data. In this paper we develop efficient bottleneck feature (BNF) extrac…

cs.SD201721 cited

Now Playing: Continuous low-power music recognition

Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo +8

Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entir…