A loudspeaker response interpolation model based on one-twelfth octave interval frequency measurements

A practical loudspeaker frequency response interpolation model is developed using a modification of the Tuneable Approximate Piecewise Linear Regression (TAPLR) model that can provide a complete magnitude and phase response over the full frequency range of the loudspeaker. This is achieved by first taking standard one-twelfth octave frequency interval acoustic intensity measurements at a one meter distance in front of the loudspeaker. These measurements are inserted directly into the formulation, which then requires only minimal tuning to achieve a magnitude response model to better than +/- 1 dB error as compared with the magnitude of the Fourier transform of the impulse response for typical hi-fi loudspeakers. The Hilbert transform can then be used to compute the corresponding phase response directly from the resulting magnitude response. Even though it is initially based on consecutive piecewise linear sections this new model provides a continuous smooth interpolation between the measured values that is much more satisfactory than normal piecewise linear segment interpolation and much simpler to do than polynomial interpolation. It only requires the tuning of a single parameter to control the degree of smoothness from a stair step response at one extreme to a straight mean horizontal line at the other. It is easy to find the best tuning parameter value in between these two extremes by either trial and error or by the minimisation of a mean squared interpolation error

The modified probabilistic neural network as a nonlinear correlator detector

A nonlinear correlator detector for the detection of a signal class with some intra class variance is developed using the modified probabilistic neural network and the general regression neural network. An application, involving the detection of regular tone bursts transmitted over a poor and noisy radio channel subjected to fading, random noise and impulse noise effects, is used to show the effectiveness of the method as compared to a linear correlato

A multi-category decision support framework for the Tennessee Eastman problem

The paper investigates the feasibility of developing a classification framework, based on support vector machines, with the correct properties to act as a decision support system for an industrial process plant, such as the Tennessee Eastman process. The system would provide support to the technicians who monitor plants by signalling the occurrence of abnormal plant measurements marking the onset of a fault condition. To be practical such a system must meet strict standards, in terms of low detection latency, a very low rate of false positive detection and high classification accuracy. Experiments were conducted on examples generated by a simulation of the Tennessee Eastman process and these were preprocessed and classified using a support vector machine. Experiments also considered the efficacy of preprocessing observations using Fisher Discriminant Analysis and a strategy for combining the decisions from a bank of classifiers to improve accuracy when dealing with multiple fault categories

Continuous-time adaptive critics

A continuous-time formulation of an adaptive critic design (ACD) is investigated. Connections to the discrete case are made, where backpropagation through time (BPTT) and real-time recurrent learning (RTRL) are prevalent. Practical benefits are that this framework fits in well with plant descriptions given by differential equations and that any standard integration routine with adaptive step-size does an adaptive sampling for free. A second-order actor adaptation using Newton's method is established for fast actor convergence for a general plant and critic. Also, a fast critic update for concurrent actor-critic training is introduced to immediately apply necessary adjustments of critic parameters induced by actor updates to keep the Bellman optimality correct to first-order approximation after actor changes. Thus, critic and actor updates may be performed at the same time until some substantial error build up in the Bellman optimality or temporal difference equation, when a traditional critic training needs to be performed and then another interval of concurrent actor-critic training may resum

An exploratory analysis of non-Poisson temporal behaviour in snapping shrimp noise

Snapping shrimp are a well known interference source for underwater sonar and communication systems, particularly in shallow and harbour waters. The noise produced by snapping shrimp is highly impulsive and the amplitude statistics are non-Gaussian. Impulsive noise is most often modelled in a way that implicitly assumes that the temporal statistics are Poisson. The Poisson assumption implies that a snap from any shrimp is completely independent of snaps from other shrimp. This paper reports on an exploratory analysis of non-Poisson temporal behaviour in snapping shrimp noise using real acoustic data from different geographic locations in Australian coastal waters. The analysis makes use of various statistical techniques applied to snaps detected in high-pass filtered data using a threshold technique. Attempts are made to eliminate multi-path effects, which can introduce correlations between snap arrivals, from other possible effects such as interactions between shrimp. The results are compared and contrasted between different geographic locations

Continuous adaptive critic designs

A continuous formulation of an adaptive critic design (ACD) is investigated. Connections to the discrete case are made, where backpropagation through time (BPTT) and realtime recurrent learning (RTRL) are prevalent. A second order actor adaptation, based on Newton's method, is established for fast actor convergence. Also, a fast critic update for concurrent actor-critic training is outlined that keeps the Bellman optimality correct to first order approximation after actor changes

The classification of sheep and goat feeding phases from acoustic signals of jaw sounds

This paper describes and documents investigatory work for the detection and measurement of sheep rumination and mastication time periods from jaw sounds transmitted through the skull. The rumination and mastication time periods were determined by a neural network classifier using a combination of time and frequency domain features extracted from successive 10 second acoustic signal lengths. It is shown that spectral features contain most of the information required for good classification

A fast adaptive neural network system for intelligent control

An intelligent control system needs to adapt to new dynamics very quickly but also retain knowledge of past dynamics to be able to act effectively and quickly for repeat occurrences. One solution is to model the system with two neural networks in parallel whereby one network is trained a priori with a wide range of historical dynamics while the second one, is allowed to adapt itself to make up the differences between the first model and the real-time dynamics. Within this scheme, as the second network is called to adapt itself, the first one can be progressively trained to learn the new dynamics without adversely affecting the old training. A strategy of this type can be achieved very effectively the modified probabilistic neural network because it is constructed with local radial kernel functions and its adaptation mechanism is computationally simple and very fast. This is demonstrated using a complex nonlinear system whose characteristics suddenly change after initial training and then switch back to the original characteristics. Comparisons are made with other networks to show the important advantages of the modified probabilistic neural network

A tunable approximately piecewise linear model derived from the modified probabilistic neural network

A simple model, which can be adjusted by a single smoothing parameter continuously from the best piecewise linear model in each linear subregion to the best approximately piecewise linear model overall, is developed for multivariate general nonlinear regression. The model provides an accurate, smooth, approximately piecewise linear model to cover the entire data space. It provides a logical basis for extrapolation to regions not represented by training data, based on the closest piecewise linear model. This model has been developed by making relatively minor changes to the form of a modified probabilistic neural network (MPNN), which is a network that id used for general nonlinear regression. The MPNN structure allows it to model data by weighting piecewise linear models associated with each of the network's radial basis functions in the data spac