This paper deals with the historical development of optical communication systems and their failures initially. Then the different generations in optical fiber communication along with their features are discussed. Some aspects of total internal reflection, different types of fibers along with their size and refractive index profile, dispersion and loss mechanisms are also mentioned. Finally the general system of optical fiber communication is briefly mentioned along with its advantages and limitations. Future soliton based optical fiber communication is also highlighted.
Audio classification is a difficult task because of the issue of extracting and choosing the optimum audio features. To reduce the computational complication from existing methods, this study proposes a feature-selection method based on modified bacterial foraging optimisation algorithm (MBFOA) for classification of audio signals. Enhanced mel-frequency cepstral coefficient and enhanced power normalised cepstral coefficients with peak and pitch are estimated the signal feature and optimised using MBFOA with the fitness function. Using the probabilistic neural network, the audio signal is classified into music and speech signal. Then, if the signal is music, the signal is classified as cello, clarinet, flute etc. If the signal is detected as a speech, then it is again classified as male or female voice. This approach shows that it is possible to boost the classification accuracy by using different features and optimisation technique.
Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.
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