True Spatio-Temporal Detection and Estimation for Functional Magnetic Resonance Imaging.

The development of fast imaging in magnetic resonance imaging (MRI) makes it possible for researchers in various fields to investigate functional activities of the human brain with a unique combination of high spatial and temporal resolution. A significant task in functional MRI data analysis is to develop a detection statistic for activation, showing subject’s localized brain responses to pre-specified stimuli. With rare exceptions in FMRI, these detection statistics have been derived from a measurement model under two main assumptions: spatial independence and space-time separability of background noise. One of the main goals of this thesis is to remove these assumptions which have been widely used in existing approaches. This thesis makes three main contributions:(1) a development of a detection statistic based on a spatiotemporally correlated noise model without space-time separability, (2) signal and noise modeling to implement the proposed detection statistic, (3) a development of a detection statistic that is robust to signal-to-noise ratio (SNR), Rician activation detection. For the first time in FMRI, we develop a properly formulated spatiotemporal detection statistic for activation, based on a spatiotemporally correlated noise model without space-time separability. The implementation of the developed detection statistic requires joint signal and noise modeling in three or four dimensions, which is non-trivial statistical model estimation. We complete the implementation with the parametric cepstrum, allowing dramatic reduction of computations in model fitting. These two are totally new contributions to FMRI data analysis. As byproducts, a novel test procedure for space-time separability is proposed and its asymptotic power is analyzed. The developed detection statistic and conventional statistics involving spatial smoothing by Gaussian kernel are compared through a model comparison technique and asymptotic relative efficiency. Most methods in FMRI data analysis are based on magnitude voxel time courses and their approximation by a Gaussian distribution. Since the magnitude images, in fact, obey Rician distribution and the Gaussian approximation is valid under a high SNR assumption, Gaussian modeling may perform poorly when SNR is low. In this thesis, we develop a detection statistic from a Rician distributed model, allowing a robust activation detection regardless of SNR.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57634/2/nohjoonk_1.pd

Real time speaker recognition using MFCC and VQ

Speaker Recognition is a process of automatically recognizing who is speaking on the basis of the individual information included in speech waves. Speaker Recognition is one of the most useful biometric recognition techniques in this world where insecurity is a major threat. Many organizations like banks, institutions, industries etc are currently using this technology for providing greater security to their vast databases.Speaker Recognition mainly involves two modules namely feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the speaker’s voice signal that can later be used to represent that speaker. Feature matching involves the actual procedure to identify the unknown speaker by comparing the extracted features from his/her voice input with the ones that are already stored in our speech database.In feature extraction we find the Mel Frequency Cepstrum Coefficients, which are based on the known variation of the human ear’s critical bandwidths with frequency and these, are vector quantized using LBG algorithm resulting in the speaker specific codebook. In feature matching we find the VQ distortion between the input utterance of an unknown speaker and the codebooks stored in our database. Based on this VQ distortion we decide whether to accept/reject the unknown speaker’s identity. The system I implemented in my work is 80% accurate in recognizing the correct speaker.In second phase we implement on the acoustic of Real Time speaker ecognition using mfcc and vq on a TMS320C6713 DSP board. We analyze the workload and identify the most timeconsuming operations

Study of Speaker Recognition Systems

Speaker Recognition is the computing task of validating a user’s claimed identity using characteristics extracted from their voices. This technique is one of the most useful and popular biometric recognition techniques in the world especially related to areas in which security is a major concern. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. Speaker recognition can be classified into identification and verification. Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker verification, on the other hand, is the process of accepting or rejecting the identity claim of a speaker. The process of Speaker recognition consists of 2 modules namely: - feature extraction and feature matching. Feature extraction is the process in which we extract a small amount of data from the voice signal that can later be used to represent each speaker. Feature matching involves identification of the unknown speaker by comparing the extracted features from his/her voice input with the ones from a set of known speakers. Our proposed work consists of truncating a recorded voice signal, framing it, passing it through a window function, calculating the Short Term FFT, extracting its features and matching it with a stored template. Cepstral Coefficient Calculation and Mel frequency Cepstral Coefficients (MFCC) are applied for feature extraction purpose. VQLBG (Vector Quantization via Linde-Buzo-Gray), DTW (Dynamic Time Warping) and GMM (Gaussian Mixture Modelling) algorithms are used for generating template and feature matching purpose

The variational approach in spectral estimation

This is a very personal point of view of the underlying ideas that yields most of the currently reported spectral estimation techniques, procedures and algorithms. After a brief introduction on non-parametric spectral estimation, which includes a shutle aspect about how to use averaging in DFT based methods, the paper describes the potential of the variational approach in deriving already reported estimates and the way out to obtain new ones. Finally, and as the second approach of high interest in spectral estimation, the design and extensions of data-dependent filters for spectral estimation is reported.Peer ReviewedPostprint (published version