Real-Time Audio Subjective Quality Assessment in Packet Networks

The goal of this paper is to show how Artificial Neural Networks (ANNs) can be used to mimic the way human subjects estimate the quality of audio signals when distorted by changes in several parameters that affect the transmitted audio quality. To validate the approach, we carried out the subjective quality experiment for speech signals distorted by different values of IP-network parameters (e.g., loss rate, loss distribution, packetization interval, etc.), and changes in the encoding algorithm used to compress the original signal. Our results allow us to show that ANNs can capture the non-linear mapping, between certain characteristics of audio signals and a subjective five points quality scale, "built" by a group of human subjects when participating in a subjective quality experiment, creating, in this way, an "Inter-Subjective" Neural Network (INN) model that might effectively "evaluate", in real time, audio quality in packet switched networks