Pornographic Audios Detection Using MFCC Features and Vector Quantization

Abstract

Identifying the phonetic characteristics of speakers is an important branch of speech recognition. The audition system of human being is an ideal speaker recognition system. MFCC (Mel-frequency cepstral coefficients) characterizes the auditory features of humans effectively, and thus has been widely used in practice. This paper explores applying MFCC and VQ (vector quantization) algorithms on the pornographic audios detection. Firstly, MFCC of selected pornographic audios are extracted and then encoded into codebooks using VQ algorithm, Secondly, all of the codebooks obtained will be averaged to get an average codebook, Finally, the type of any newly input audio belonging to, either pornographic or non-pornographic, will be determined by measuring the Euclidean distance between the average codebook and its own codebook. Experiment results show that the algorithm can detect pornographic audios effectively.