N. Esfandian; F. Jahani bahnamiri; S. Mavaddati
Abstract
This paper proposes a novel method for voice activity detection based on clustering in spectro-temporal domain. In the proposed algorithms, auditory model is used to extract the spectro-temporal features. Gaussian Mixture Model and WK-means clustering methods are used to decrease dimensions of the spectro-temporal ...
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This paper proposes a novel method for voice activity detection based on clustering in spectro-temporal domain. In the proposed algorithms, auditory model is used to extract the spectro-temporal features. Gaussian Mixture Model and WK-means clustering methods are used to decrease dimensions of the spectro-temporal space. Moreover, the energy and positions of clusters are used for voice activity detection. Silence/speech is recognized using the attributes of clusters and the updated threshold value in each frame. Having higher energy, the first cluster is used as the main speech section in computation. The efficiency of the proposed method was evaluated for silence/speech discrimination in different noisy conditions. Displacement of clusters in spectro-temporal domain was considered as the criteria to determine robustness of features. According to the results, the proposed method improved the speech/non-speech segmentation rate in comparison to temporal and spectral features in low signal to noise ratios (SNRs).
S. Mavaddati; S. Mavaddati
Abstract
Development of an automatic system to classify the type of rice grains is an interesting research area in the scientific fields associated with modern agriculture. In recent years, different techniques are employed to identify the types of various agricultural products. Also, different color-based and ...
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Development of an automatic system to classify the type of rice grains is an interesting research area in the scientific fields associated with modern agriculture. In recent years, different techniques are employed to identify the types of various agricultural products. Also, different color-based and texture-based features are used to yield the desired results in the classification procedure. This paper proposes a classification algorithm to detect different rice types by extracting features from the bulk samples. The feature space in this algorithm includes the fractal-based features of the extracted coefficients from the wavelet packet transform analysis. This feature vector is combined with other texture-based features and used to learn a model related to each rice type using the Gaussian mixture model classifier. Also, a sparse structured principal component analysis algorithm is applied to reduce the dimension of the feature vector and lead to the precise classification rate with less computational time. The results of the proposed classifier are compared with the results obtained from the other presented classification procedures in this context. The simulation results, along with a meaningful statistical test, show that the proposed algorithm based on the combinational features is able to detect precisely the type of rice grains with more than 99% accuracy. Also, the proposed algorithm can detect the rice quality for different percentages of combination with other rice grains with 99.75% average accuracy.
