Impulsive Environment Sound Detection by Neural Classification of Spectrogram and Mel-Frequency Coefficient Images

The problem of automatic detecting impulsive sounds such as human sound (screams, shout), gun shots, machine gun, thunder, fire alarm, and car horn are useful for hearing impairment person. In this paper, instead of filtering the frequency of each sound for identifying types of sound, the frequency of sound is transformed into a recognizable image. The transformation is based on audio spectrogram (Power Spectrum) and Mel-frequency cepstral coefficients (MFCC). The images of both power spectrum and Mel-frequency spectrum are used as the inputs for an artificial neural network to recognize the corresponding sound. The proposed technique is tested with six different types of sound, i.e. machine gun, human scream, gun shot, thunder, fire alarm, and car horn from a sound database containing more than one hour of six different impulsive sounds. The experimental results on impulsive sounds detection using a spectrogram with feed-forward neuron network can effectively detect the segments of impulsive sound region in audio signal with more than 94% accuracy.

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