Machine Learning Analysis of Binaural Rowing Sounds
Leonard Johard*, Emanuele Ruffaldi*, Pablo Hoffmann† and Alessandro Filippeschi*
Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition methodology and the evaluation of different machine learning techniques for classifying rowing-sound data. We see that a combination of principal component analysis and shallow networks perform equally well as deep architectures, while being much faster to train.
© Owned by the authors, published by EDP Sciences, 2011