Prof-life-log: audio environment detection for naturalistic audio streams
Ali Ziaei, Abhijeet Sangwan, John H. L. Hansen · 2012
In this study, we develop a new system for real world audio environment matching. Environment detection within unknown audio streams requires a system that operates in an unsupervised manner since it will be faced with unknown environments without prior information. In addition, the overall solution should be computationally efficient for large audio collection. In the proposed approach, a Gaussian mixture model(GMM) is trained on large amounts of unlabeled audio data and used as a background acoustic model. Subsequently, an acoustic signature vector (ASV) is computed for each environment. Here, the ASV vector is designed to capture the unique acoustic characteristics of an environment. Using the ASV vectors, we demonstrate that it is possible to compute an effective similarity measure between two acoustic environments. We demonstrate the performance of the proposed system on real-world audio data, and compare it to a traditional GMM-UBM (Universal Background Model) system. Experiments show that our system achieves an equal error rate (EER) that is +35% better than a baseline GMM-UBM system.