Nurgun Erdol

Wavelet transform based adaptive filters: analysis and new results

In this paper the wavelet transform is used in an adaptive filtering structure. The coefficients of the adaptive filter are updated by the help of the least mean square (LMS) algorithm. First, the wavelet transform based adaptive filter (WTAF) is described and it is analyzed for its Wiener optimal solution. Then the performance of the WTAF is studied by the help of learning curves for three different convergence factors: (1) constant convergence factor, (2) time-varying convergence factor, and (3) exponentially weighted convergence factor. The exponentially weighted convergence factor is proposed to introduce scale-based variation to the weight update equation. It is shown for two different sets of data that the rate of convergence increases significantly for all three WTAF structures as compared to that of time-domain LMS. The high convergence rates of the WTAF give us reason to expect that it will perform well in tracking rapid changes in a signal.

Recovery of missing speech packets using the short-time energy and zero-crossing measurements

A waveform substitution technique using interpolation based on the slowly varying speech parameters of short-time energy and zero-crossing information is developed for a packetized speech communication system. The system uses 64-kb conventional pulse code modulation (PCM) for encoding and takes advantage of active talkspurts and silence intervals to increase the efficiency of utilizing a digital link. The short-time energy and information on the zero-crossings needed for the purpose of determining talkspurts are transmitted in a preceding packet. Hence, when a packet is pronounced lost, its envelope and frequency characteristics are obtained from a previous packet and used to synthesize a substitution waveform which is free of annoying sounds that are due to abrupt changes in amplitude. >

Performance of wavelet transform based adaptive filters

The use of the wavelet transform in transform domain adaptive filtering (WTAF) is analyzed for performance as measured by learning curves. It is shown that the minimum mean squared error improves significantly with the use of the self-orthogonalizing wavelet transform least mean square (WLMS). An exponentially weighted convergence factor is proposed to introduce scale-based variation to the weight update equation. Simulations for learning curves are obtained by using a conventional smooth signal with sinusoidal components as well as a nonsmooth signal recorded in an electrically noisy environment. The latter signal consists of periodic as well as randomly occurring signals from multiple sources.<<ETX>>

Wavelet modulation: a prototype for digital communication systems

The wavelet transform has had an explosive development as a powerful tool of signal representation. It has been used successfully in many fields such as image processing, data compression and signal processing. Due to the fact that a wavelet can be chosen from a flexible class of analysis functions with different time-frequency localization properties, its performance surpasses that of the Fourier transform when dealing with transient, or time localized signals. It also has some applications in communication systems. The authors discuss the possibility of using wavelet functions in a digital communication system. It is shown to possess advantages of bandwidth, noise performance and complexity of synchronization.

Using local binary patterns as features for classification of dolphin calls

An image processing technique called Local Binary Patterns (LBP) has been explored for its ability to generate feature vectors for dolphin vocalization classification. The LBP operator eliminates the need for contour tracing, denoising, and other prior processing. In an experimental study of classifying dolphin whistle types, the performance of the LBP operation was compared with that of the popular contour-based Time-Frequency Parameters (TFP) approach. The preliminary experimental results illustrate that the LBP method produces more consistent classifier accuracy of dolphin whistle calls even when the contour shapes are complex and populated with impulsive clicks and anthropogenic harmonics.

Time-varying, saturating feedback control of linear systems under state and control constraints

The problem of obtaining a linear discrete-time system under state and control linear constraints is studied. The time-varying linear control law is proposed. It is shown that application of a time-varying controller instead of a time-invariant one improves the system performance. The control input may saturate while the stability of the closed-loop system is preserved.<<ETX>>

On the discrete wavelet transform and shiftability

We analyze the relationship between the change that is observed in the wavelet coefficients when a signal is time shifted and the time and frequency distributions of the wavelet functions. We address the effects of shift variance and show how it can be useful.<<ETX>>

HMM Based Spectral Frequency Line Tracking: Improvements and New Results

This paper considers the application of hidden Markov models to the problem of tracking frequency lines in spectrograms of strongly non-stationary signals such as encountered in aero-acoustics and sonar where tracking difficulties arise from low SNR and large variances associated with spectral estimates. In the proposed method, we introduce a novel method to determine the observation (measurement) likelihoods by interpolation between local maxima. We also show that use of low variance autoregressivemultitaper (ARMT) spectral estimates results in improved tracking. The frequency line is tracked using the forward-backward algorithm

Comparison of two methods for detection of North Atlantic Right Whale upcalls

In this paper, a study is carried out for detecting North Atlantic Right Whale upcalls with measurements from passive acoustic monitoring devices. Preprocessed spectrograms of upcalls are subjected to two different tasks, one of which is based on extraction of time-frequency features from upcall contours, and the other that employs a Local Binary Pattern operator to extract salient texture features of the upcalls. Then several classifiers are used to evaluate the effectiveness of both the contour-based and texture-based features for upcall detection. Detection results reveal that popular classifiers such as Linear Discriminant Analysis, Support Vector Machine, and TreeBagger can achieve high detection rates. Furthermore, using LBP features for call detection shows improved accuracy of about 3% to 4% over time-frequency features when an identical classifier is used.

A new approach for classification of dolphin whistles

This paper presents a novel approach to categorize dolphin whistles into various types. Most accurate methods to identify dolphin whistles are tedious and not robust, especially in the presence of ocean noise. One of the biggest challenges of dolphin whistle extraction is the coexistence of short-time duration wide-band echo clicks with the whistles. In this research a subspace of select orientation parameters of the 2-D Gabor wavelet frames is utilized to enhance or suppress signals by their orientation. The result is a Gabor image that contains a noise free grayscale representation of the fundamental dolphin whistle which is resampled and fed into the Sparse Representation Classifier. The classifier uses the l1-norm to select a match. Experimental studies conducted demonstrate: (a) a robust technique based on the Gabor wavelet filters in extracting reliable call patterns, and (b) the superior performance of Sparse Representation Classifier for identifying dolphin whistles by their call type.