Introduction By using EEG to collect EEG data from our brain, sometimes we will need to know which frequency band does our signal fall in to provide more features and information for later tasks. In this experiment, we are about to analyze a signal using Fast Fourier Transform (FFT) and Power Spectral Density (PSD). There […]Read more "Spectrum Analysis of EEG Signal"
Introduction The advantage of neural networks over other methods is due to their non-linearity. The non-linearity is caused by the linear combinations of the activation functions used. The activation functions that we will be using here is Sigmoid and ReLU. Let be the output of a neuron after a linear combination of its input neurons […]Read more "Neural Network for Multiclass Classification"
Introduction Dynamic Time Warping is an algorithm used to match two speech sequence that are same but might differ in terms of length of certain part of speech (phones for example). Here, we’ll not be using phone as a basic unit but frames that are obtained from MFCC features that are obtained from feature extraction […]Read more "Dynamic Time Warping for Speech Recognition"
Introduction GMM vs K-Means First, we’ll have to understand what are hard decisions and soft decisions . Hard Decision A data point is clustered to a single cluster and the results are final. Soft Decision A data point is modeled by a distribution of clusters, thus it will be probabilistically defined and there’s no definite […]Read more "GMM-Based Speaker Recognition"
Introduction There’re 3 major methods on working with classification: Discriminant Function Probabilistic Generative Model Probabilistic Discriminative Model The first method is brute-force method which is what neural networks uses. It consist of least square classification, Fisher’s linear discriminant and perceptron algorithm. Our focus will be on the following 2 models which involves a probabilistic viewpoint. […]Read more "Linear Models for Classification"