New results in fuzzy pattern classification of background noise
Francesco Beritelli, S. Casale, Giuseppe Ruggeri · 2002
This paper proposes a background noise classifier based on a new, computationally simple, robust set of acoustic features. Complementary to our previous work (1998), 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; and 3) a new set of robust acoustic parameters. We have maintained the pattern recognition approach proposed previously in which the matching phase is performed using a set of trained fuzzy rules. The improved version of the fuzzy noise classifier has been assessed in terms of misclassification percentage and compared with the quadratic Gaussian classifier.