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.

This paper proposes a method for extracting the fundamental frequency of voiced speech from distant speech signals. The method is based on the impulse-like nature of excitation in voiced speech. The characteristics of impulse-like excitation are extracted by filtering the speech signal through a cascade of resonators located at zero frequency. The resulting filtered signal preserves information specific to the fundamental frequency, in the sequence of positive-to-negative zero crossings. Also, the filtered signal is free from the effects of resonances of the vocal tract. An estimate of the fundamental frequency is derived from the short-time spectrum of the filtered signal. This estimate is used to remove spurious zero crossings in the filtered signal. The proposed method depends only on the strengths of impulse-like excitations in the direct component of distant speech signals, and not on the similarity of speech signal in successive glottal cycles. Hence, the method is robust to the effects of reverberation and noise. Performance of the method is evaluated using a database of close-speaking and distant speech signals. Experiments show that the accuracy of the proposed method is significantly higher than that of existing methods based on time-domain and frequency-domain processing.