Audiogmenter: a MATLAB toolbox for audio data augmentation - Gianluca Maguolo; Michelangelo Paci; Loris Nanni; Ludovico Bonan (Applied Computing and Informatics)
Purpose – Create and share a MATLAB library that performs data augmentation algorithms for audio data. This study aims to help machine learning researchers to improve their models using the algorithms proposed by the authors. Design/methodology/approach – The authors structured our library into methods to augment raw audio data and spectrograms. In the paper, the authors describe the structure of the library and give a brief explanation of how every function works. The authors then perform experiments to show that the library is effective. Findings – The authors prove that the library is efficient using a competitive dataset. The authors try multiple data augmentation approaches proposed by them and show that they improve the performance. Originality/value – A MATLAB library specifically designed for data augmentation was not available before. The authors are the first to provide an efficient and parallel implementation of a large number of algorithms.