New Results in Fuzzy Pattern Classification of Background Noise

This paper proposes a background noise classifier based on a new, computationally simple, robust set of acoustic features. Complementary to a previous work [1], reporting on the first studies carried out by the authors on background noise classification, this paper mainly presents: 1) a criterion to group a large range of environmental noise into a reduced set of classes of noise with similar acoustic characteristics; 2) a larger set of background noise together with a new multilevel classification architecture; 3) a new set of robust acoustic parameters. We have maintained the pattern recognition approach proposed in [1] in which the matching phase is performed using a set of trained fuzzy rules. The improved version of the Fuzzy Noise Classifier (FNC) has been assessed in terms of misclassification percentage and compared with a Quadratic Gaussian Classifier (QGC).