Real-world acoustic event detection

Acoustic Event Detection (AED) aims to identify both timestamps and types of events in an audio stream. This becomes very challenging when going beyond restricted highlight events and well controlled recordings. We propose extracting discriminative features for AED using a boosting approach, which outperform classical speech perceptual features, such as Mel-frequency Cepstral Coefficients and log frequency filterbank parameters. We propose leveraging statistical models better fitting the task. First, a tandem connectionist-HMM approach combines the sequence modeling capabilities of the HMM with the high-accuracy context-dependent discriminative capabilities of an artificial neural network trained using the minimum cross entropy criterion. Second, an SVM-GMM-supervector approach uses noise-adaptive kernels better approximating the KL divergence between feature distributions in different audio segments. Experiments on the CLEAR 2007 AED Evaluation set-up demonstrate that the presented features and models lead to over 45% relative performance improvement, and also outperform the best system in the CLEAR AED Evaluation, on detection of twelve general acoustic events in a real seminar environment.