Normalization of Partly Overlapping Audio Recordings from the Same Event Based on Relative Signal Powers

Abstract

Exploiting correlations in the audio, several works in the past have demonstrated the ability to automatically match and synchronize user-generated video or audio files of the same event. Such tools solve for the unknown starting and ending time of each available recording along the event time-line and open the way for collaborative content production approaches. However, a source of difficulty for collaborative processing approaches related to audio is the fact that the different audio recordings may be available at significantly different signal levels. In this paper, we present a normalization approach to automatically define gains for all the recordings so that the variations in the signal levels among different recordings are suppressed. We show that normalization is trivial when all recordings share the same time support but the same process is non-trivial when the recordings partly overlap along time, especially if the acoustic event is characterized by high dynamic variations. We demonstrate the efficiency of the proposed approach under various conditions based on real examples of user-generated audio recordings.