Iranian Journal of Electrical and Electronic Engineering

In this paper, a special preprocessing operations (filter) is proposed to decrease
the effects of noise of textures. This filter using average of circular neighbor points (Cmean)
to reduce noise effect. Comparing this filter with other average filters such as square
mean filter and square median filter indicates that it provides more noise reduction and
increases the classification accuracy. After applying filter to noisy textures some Local
Binary Pattern (LBP) variants are used for feature extraction. The Implementation part for
noisy textures of Outex, UIUC and CUReT datasets shows that using proposed filter
increases the classification accuracy significantly. Furthermore, a simple and new technique
is proposed that increases the speed of c-mean filter noticeably.

Automatic recognition of speech emotional states in noisy conditions has become an important research topic in the emotional speech recognition area, in recent years. This paper considers the recognition of emotional states via speech in real environments. For this task, we employ the power normalized cepstral coefficients (PNCC) in a speech emotion recognition system. We investigate its performance in emotion recognition using clean and noisy speech materials and compare it with the performances of the well-known MFCC, LPCC, RASTA-PLP, and also TEMFCC features. Speech samples are extracted from the Berlin emotional speech database (Emo DB) and Persian emotional speech database (Persian ESD) which are corrupted with 4 different noise types under various SNR levels. The experiments are conducted in clean train/noisy test scenarios to simulate practical conditions with noise sources. Simulation results show that higher recognition rates are achieved for PNCC as compared with the conventional features under noisy conditions.