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The calculation of time delay between a signal and its echo received at a microphone has been proven to be a useful parameter. Speech enhancement, speaker localization, speech and speaker recognition are few applications of TDE methods. We are implementing various methods for the estimation of time delay. These methods are implemented in MATLAB. The reason for choosing the MATLAB as the analysis and simulation tool is that it has more flexible choices to support the simulation and is easy to do modification or data recording. These methods are crosscorrelation (CC), phase transform (PHAT). Various time-delay estimation techniques based on the cross-correlation functions are compared through simulations and measurements. Their simulation results are compared in terms of computation complexity, hardware implementation, precision, and accuracy.

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PurposeThis work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.Design/methodology/approachIn this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.FindingsFor the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.Originality/valueEpilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.