Supervised machine learning relies on the accessibility of large datasets of annotated data.This is essential since small datasets generally lead to overfitting when training high-dimensional machine-learning models.Since the manual annotation of such Bone Saws large datasets is a long, tedious and expensive process, another possibility is to artificially increase the size of the dataset.This is known as data augmentation.
In this paper we provide an in-depth analysis of two data augmentation methods: sound transformations and sound segmentation.The first transforms a music track to a set of new music tracks by applying processes such as pitch-shifting, time-stretching or filtering.The second one splits a long sound signal into a set of shorter time segments.We study the effect of these two techniques PEPPERMINT LOTION (and the parameters of those) for a genre classification task using public datasets.
The main contribution of this work is to detail by experimentation the benefit of these methods, used alone or together, during training and/or testing.We also demonstrate their use in improving the robustness of potentially unknown sound degradations.By analyzing these results, good practice recommendations are provided.