A SEMI-SUPERVISED LEARNING APPROACH TO ONLINE AUDIO BACKGROUND DETECTION

We present a framework for audio background modeling of complex and unstructured audio environments. The determination of background audio is important for understanding and predicting the ambient context surrounding an agent, both human and machine. Our method extends the online adaptive Gaussian Mixture model technique to model variations in the background audio. We propose a method for learning the initial background model using a semi-supervised learning approach, This information is then integrated into the online background determination process, providing us with a more complete background model. We show that we can utilize both labeled and unlabeled data to improve audio classification performance. By incorporating prediction models in the determination process, we can improve the background detection performance even further. Experimental results on real data sets demonstrate the effectiveness of our proposed method.