Audio classification based on fuzzy-rough nearest neighbour clustering

In the time of digital information, audio data has become an important part in many modern computer applications. Automatic classification based on audio content has been considered as an important way to cope with the problem of audio structuration. In this paper, we present an improved algorithm based on FRNNC (Fuzzy-rough nearest neighbour clustering), which derive from FRNN algorithm and have combined clustering algorithm. In our work, we extract audio features from the MDCT domain and form feature vectors by introduce the concept of feature granularity, and then apply the FRNNC algorithm in audio classification. The experimental results show that our classification method not only greatly reduces the processing time of classification, but also improves the classification accuracy.