Fusion of spectral and time domain features for crowd noise classification system

In this paper, we explore features related to spectral and time domain for classification of crowd noise. Spectral information is represented by mel-frequency cepstral coefficients (MFCC) and spectral flatness measure (SFM), whereas time domain information is represented by short-time energy (STE) and zero-cross rate (ZCR). For carrying out these studies, crowd noise data collected from railway stations and book fairs have been used. In this study, two categories of crowd noise, namely, no crowd and crowd, are used. Support Vector Machines (SVM) are used to capture the discriminative information between the above mentioned noise categories, from the spectral and time domain features. The SVM models are developed separately using spectral and time domain features. The classification performance of the developed SVM models using spectral and time domain features is observed to be 91.35% and 84.65%, respectively. In this work, we have also examined the performance of the crowd noise classification system by combining the spectral and time domain information at feature and score levels. The classification performance using feature and score level fusion is observed to be 93.10% and 96.25% respectively.