TOWARDS BLIND QUALITY ASSESSMENT OF CONCERT AUDIO RECORDINGS USING DEEP NEURAL NETWORKS

Live music audio and video recordings represent a large percentage of the huge amount of User Generated Content (UGC) that is available on the internet today. Applications and services related to the management and consumption of this content may significantly benefit from tools able to produce a subjective score of the audio quality. In this work, we apply different Deep Neural Network (DNN) architectures to a simple binary classification problem, that of deciding whether a musical recording is user-generated or of professional quality. Showing that we are able to efficiently address this binary classification problem, we gain some useful insight about factors that may assist the design and affect the performance of a future system that would be able to address the more general problem of blind audio quality assessment.