Impress: A Machine Learning Approach to Soundscape Affect Classification for a Music Performance Environment

Soundscape composition in improvisation and performance contexts involves manyprocesses that can become over- whelming for a performer, impacting on thequality of the composition. One important task is evaluating the mood of acomposition for evoking accurate associations and mem- ories of a soundscape. Anew system that uses supervised machine learning is presented for theacquisition and re- altime feedback of soundscape affect. A model of sound-scape mood is created by users entering evaluations of au- dio environmentsusing a mobile device. The same device then provides feedback to the user ofthe predicted mood of other audio environments. We used a features vector ofTotal Loudness and MFCC extracted from an audio signal to build a multipleregression models. The evaluation of the system shows the tool is effective inpredicting soundscape affect.