Audio Features for Noisy Sound Segmentation

Automatic audio classification usually considers sounds as music, speech, silence or noise, but works about the noise class are rare. Audio features are generally specific to speech or music signals. In this paper, we present a new audio feature sets that lead to the definition of four classes: colored, pseudo-periodic, impulsive and sinusoids within noises. This classification relies on works about the perception of noises. This audio feature set is experimented for noisy sound segmentation. Noise-to-noise transitions are characterized by means of statistical decision model based on Bayesian framework. This statistical method has been trained and experimented both on synthetic and real audio corpus. Using proposed feature set increases the discriminant power of Bayesian decision approach compared to a usual feature set.