Data Augmentation Techniques for Robust Audio Analysis
Ville-Veikko Eklund · Tampere University Institutional Repository (Tampere University) · 2019
Having large amounts of training data is necessary for the ever more popular neural networks to perform reliably. Data augmentation, i.e. the act of creating additional training data by performing label-preserving transformations for existing training data, is an efficient solution for this problem. While increasing the amount of data, introducing variations to the data via the transformations also has the power to make machine learning models more robust in real life conditions with noisy environments and mismatches between the training and test data. In this thesis, data augmentation techniques in audio analysis are reviewed, and a tool for audio data augmentation (TADA) is presented. TADA is capable of performing three audio data augmentation techniques, which are convolution with mobile device microphone impulse responses, convolution with room impulse responses, and addition of background noises. TADA is evaluated by using it in a pronunciation error classification task, where typical pronunciation errors of Finnish people uttering English words are classified. All the techniques are tested first individually and then also in combination. The experiments are executed with both original and augmented data. In all experiments, using TADA improves the performance of the classifier when compared to training with only original data. Robustness against unseen devices and rooms also improves. Additional gain from performing combined augmentation starts to saturate only after augmenting the training data to 30 times the original amount. Based on the positive impact of TADA for the classification task, it is found that data augmentation with convolutional and additive noises is an effective combination for increasing robustness against environmental distortions and channel effects.