tinyML EMEA 2022 Dima Lvov: Sound Classification Model Robustness using Augmentation and…

tinyML EMEA 2022
Algorithms, Software & Tools session
Sound Classification Model Robustness using Augmentation and Similarity Loss
Dima LVOV, Deep Learning Algorithms Team Leader, Synaptics

Deep Neural Networks (DNN) are a popular algorithmic tool for dealing with classification and regression tasks. Sound Event Detection (SED) is a classification task of recognizing sound events captured by a microphone, and their respective temporal start and end points. High performance can be achieved using DNN in controlled lab environments, but if often deteriorates significantly in real-life scenarios including ambient noises, polyphonic environments, and room reverberation. This research presents a novel combination of similarity loss and data augmentation method in a form of pairs-training process, for achieving better robustness to the aforementioned factors. Our experiments show a superiority of the proposed method compared to a regular data augmentation scheme.

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