Ecole d'ingénieur et centre de recherche en Sciences du numérique

Sound classification using summary statistics and N-path filtering

Villamizar, Daniel; Battaglino, Daniele; Muratore, Dante G.; Hoshyar, Reza; Murmann, Boris

ISCAS 2019, IEEE International Symposium on Circuits and Systems, 26-29 May 2019, Sapporo, Japan

lways-on sound classification is a desirable but power-intensive function for a variety of emerging Internet of Everything applications. This work explores the accuracy-complexity tradeoff by using summary statistics for classifying semi-stationary sounds. Compared to contemporary solutions including deep learning, this approach requires one to three orders of magnitude fewer parameters and can therefore be trained over ten times faster. We propose a mixed-signal design using N-path filters for feature extraction to further improve energy efficiency without incurring a large accuracy penalty for a binary classification task (less than 2.5% area reduction under receiver operating characteristic curve).

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Titre:Sound classification using summary statistics and N-path filtering
Département:Sécurité numérique
Eurecom ref:5870
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Bibtex: @inproceedings{EURECOM+5870, doi = {}, year = {2019}, title = {{S}ound classification using summary statistics and {N}-path filtering}, author = {{V}illamizar, {D}aniel and {B}attaglino, {D}aniele and {M}uratore, {D}ante {G}. and {H}oshyar, {R}eza and {M}urmann, {B}oris}, booktitle = {{ISCAS} 2019, {IEEE} {I}nternational {S}ymposium on {C}ircuits and {S}ystems, 26-29 {M}ay 2019, {S}apporo, {J}apan}, address = {{S}apporo, {JAPON}}, month = {05}, url = {} }
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