Audio-based automatic detection of objectionable contents in noisy conditions using normalized segmental two-dimesional MFCC

The segmental two-dimensional Mel-frequency cepstral coefficient (STDMFCC) feature has been successfully used in recent studies to detect objectionable sounds, which implicitly represent both static and dynamic characteristics of signal. This study now proposes a new normalized STDMFCC to improve the content recognition performance in diverse noisy environments. Two tests were conducted to verify the performance of the proposed feature: First, an objectionable sound recognition test was conducted with 10-second clips to which white noises with diverse signal-to-noise ratios (SNRs) were added. The proposed feature in the test had an average error reduction rate (ERR) of 24.69% with respect to the STDMFCC. Second, a test was conducted based on the soundtrack that contained diverse channel environments and noises. The equal error rate (EER) of the proposed feature was 4.00% compared with 10.33% of STDMFCC, and the ERR was 61.29%.