An automatic approach of audio feature engineering for the extraction, analysis and selection of descriptors

Currently, it is critical to find the correct features from the audio, in order to analyze the information contained in it. This paper analyzes several feature types in audio from different points of view: time series, sound engineering, etc. In particular, the description of audio as a set of time series is not very common in the literature, and it is one of the aspects studied in this paper. Particularly, this paper proposes an automated method for feature engineering in audios, to extract, analyze and select the best features in a given context. Specifically, this paper develops a hybrid scheme of extraction of audio descriptors based on different principles and defines an automatic approach for the analysis and selection of these descriptors in a given audio context. Finally, our approach was tested on grouping tasks and compared to previous works on audio classification problems, with encouraging results.