Title: Ultra-low-power voice-activity-detector through context- and resource-cost-aware feature selection in decision trees
Authors: Lauwereins, Steven ×
Meert, Wannes
Gemmeke, Jort
Verhelst, Marian #
Issue Date: 21-Sep-2014
Host Document: Machine Learning for Signal Processing, IEEE Workshop on edition:24 pages:70-70
Conference: Machine Learning for Signal Processing, IEEE workshop on edition:24 location:Reims, France date:21-24 September 2014
Article number: 70
Abstract: Voice-activity-detectors (VADs) are an efficient way to re- duce unimportant audio data and are therefore a crucial step towards energy-efficient ubiquitous sensor networks. Current VADs, however, use computationally expensive feature ex- traction and model building algorithms with too high power requirements to be integrated in low-power sensor nodes. To drastically reduce the VAD power consumption, this paper in- troduces a decision tree based VAD with (1) a two-phase VAD operation to maximally reduce the power-hungry learning phase, (2) a scalable analog feature extraction block, and (3) context- and dynamic resource-cost-aware feature selection. Evaluation of the VAD was performed with the NOIZEUS database, demonstrating a comparable performance to SoA VADs such as Sohn and Ram ́ırez, while reducing the feature extraction power consumption up to approximately 200 fold.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:ESAT - MICAS, Microelectronics and Sensors
Informatics Section
ESAT - PSI, Processing Speech and Images
× corresponding author
# (joint) last author

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