Analysis of the of training and test data distribution for audio series classification

Анотація

The effectiveness of machine learning algorithms for any given task largely depends on the training and test datasets. This manifests itself not only in the amount of data, but also in its content (that is, its relevance for the task at hand), as well as in its organization. Generally, the common approach is to split the dataset into training and testing sets to avoid model overfitting. In addition, to achieve better metrics for the selected criteria (accuracy, learning rate, etc.) of model performance, different ratios of training and test sets are used in the partitioning. The goal of this paper is to analyze methods of data set partitioning for use in training neural networks and statistical models. One of the reviewed methods, specifically the cross-validation method, was applied to a dataset developed from the LibriSpeach corpus, an open English speech corpus based on the LirbiVox project of voluntarily contributed audio books. The result of applying the selected data partitioning method on the selected data set is demonstrated.