Acoustic scene analysis from acoustic event sequence with intermittent missing event

We propose a novel method for analyzing acoustic scenes that can sophisticatedly estimate acoustic scenes from an acoustic event sequence with intermittent missing events. On the basis of the idea that acoustic events are temporally correlated, we model the transition of acoustic events using a hidden Markov model (HMM) and estimate missing acoustic events. Then, we incorporate the transition of acoustic events in a generative process of acoustic event sequence associated with the acoustic scenes based on acoustic topic model (ATM). Since the proposed method allows us to analyze acoustic scenes from acoustic event sequences while estimating missing acoustic events, we can estimate acoustic scenes successfully and restore missing acoustic events. Evaluation results indicate that the proposed method achieves an estimation accuracy for acoustic scenes comparable to that obtained when there is no missing data. Additionally, the proposed model can estimate acoustic events that are strongly correlated with acoustic scenes in an acoustic event sequence.