Abstract
The Gaussian mixture model (GMM) has developed well both in the speech and sound recognition, but it does not perform well in the high background noisy environment. This thesis proposes a method combining short-term and long-term features to overcome this issue. Here the short-term features are Mel-frequency cepstral coefficients (MFCCs) and the long-term features are the modulation spectral vectors (MSVs) calculated in the frequency domain. The MSVs contains the envelope message of signals which is a good feature against high noise. For robustness against noise, this thesis proposes a method to learn noisy data while training on GMMs. This method could raise the recognition accuracy in the low singal-to-noise ratio (SNR) case. The method was evaluated on a database which consists of 8 different indoor sound event classes. It achieves > 80 % accuracy at 0 dB SNR.