Detection of helicopters using neural nets

Artificial neural networks (ANNs), in combination with parametric spectral representation techniques, are applied for the detection of helicopter sound. Training of the ANN detectors was based on simulated helicopter sound from four helicopters and a variety of nonhelicopter sounds. Coding techniques based on linear prediction coefficients (LPCs) have been applied to obtain spectral estimates of the acoustic signals. Other forms of the LPC parameters such as reflection coefficients, cepstrum coefficients, and line spectral pairs (LSPs) have also been used as feature vectors for the training and testing of the ANN detectors. We have also investigated the use of wavelet transform for signal de-noising prior to feature extraction. The performance of various feature extraction techniques is evaluated in terms of their detection accurac

Fuzzy controllers design using space-filling curves

We present a clustering technique for fuzzy rules based on Hilbert space-filling curves (SFC). SFC scans an n-dimensional space and reduces it to a curve, i.e. a one-dimensional line. We first introduce the Hilbert space-filling curves, and outline the algorithms for clustering and adaptive clustering which demonstrate SFC efficient self-organizing features. We then propose a SFC fuzzy inference model based on clustering the object space. The SFC fuzzy model is then used to design a fuzzy controller. The proposed method achieves a dramatic reduction of the complexity of fuzzy controller by reducing the multivariable fuzzification problem to a one dimensional spac

Fast Methods Fbr Split Codebooks

This paper presents a fast method for building and searching split codebooks for vector quantization. The proposed method is evaluated in near transparent quality vector quantization of Line Spectral Frequencies (LSF) at 24-bit per frame. The method is based on a family of fractals called Space-Filling Curves (SFC). The SF curves achieve a significant saving in the complexity of vector quantization by reducing the problem to quantization in one-dimensional space. The paper presents algorithms for the generation of the SFC mapping utilizing the self-replication feature of the curves, and a number of simulation experiments to demonstrate the effectiveness of the method. It is shown that the SFC can reduce the search complexity of split codebooks by a factor of 8-32 times with a slight degradation in the vector quantization performance

Fast Methods Fbr Split Codebooks

This paper presents a fast method for building and searching split codebooks for vector quantization. The proposed method is evaluated in near transparent quality vector quantization of Line Spectral Frequencies (LSF) at 24-bit per frame. The method is based on a family of fractals called Space-Filling Curves (SFC). The SF curves achieve a significant saving in the complexity of vector quantization by reducing the problem to quantization in one-dimensional space. The paper presents algorithms for the generation of the SFC mapping utilizing the self-replication feature of the curves, and a number of simulation experiments to demonstrate the effectiveness of the method. It is shown that the SFC can reduce the search complexity of split codebooks by a factor of 8-32 times with a slight degradation in the vector quantization performance

Fuzzy Controllers Design Using Space-Filling Curves

In this paper we present a clustering technique for fuzzy rules based on Hilbert Space-filling Curves (SFC). SFC scans an n-dimensional space and reduces it to a curve, i.e. a one-dimensional line. The paper introduces first the Hilber Space-filling curves, and outlines algorithms for clustering and adaptive clustering which demonstrate the SFC efficient self-organizing features. We then propose a SFC fuzzy inference model based on clustering the object space. The SFC fuzzy model is then used to design a fuzzy controller. The proposed method achieves a dramatic reduction of the complexity of fuzzy controller by reducing the multivariable fuzzification problem to a one dimentional space

Parametric Models For Helicopter Identification Using ANN

An artificial neural network (ANN) based helicopter identification system is proposed. The feature vectors are based on both the tonal and the broadband spectrum of the helicopter signal. ANN pattern classifiers are trained using various parametric spectral representation techniques. Specifically, linear prediction, reflection coefficients, cepstrum, and line spectral frequencies (LSF) are compared in terms of recognition accuracy and robustness against additive noise. Finally, an 8-helicopter ANN classifier is evaluated, It is also shown that the classifier performance is dramatically improved if it is trained using both clean data and data corrupted with additive noise

Parametric Models For Helicopter Identification Using ANN

An artificial neural network (ANN) based helicopter identification system is proposed. The feature vectors are based on both the tonal and the broadband spectrum of the helicopter signal. ANN pattern classifiers are trained using various parametric spectral representation techniques. Specifically, linear prediction, reflection coefficients, cepstrum, and line spectral frequencies (LSF) are compared in terms of recognition accuracy and robustness against additive noise. Finally, an 8-helicopter ANN classifier is evaluated, It is also shown that the classifier performance is dramatically improved if it is trained using both clean data and data corrupted with additive noise