A target guided subband filter for acoustic event detection in noisy environments using wavelet packets

This paper deals with acoustic event detection (AED), such as screams, gunshots, and explosions, in noisy environments. The main aim is to improve the detection performance under adverse conditions with a very low signal-to-noise ratio (SNR). A novel filtering method combined with an energy detector is presented. The wavelet packet transform (WPT) is first used for time-frequency representation of the acoustic signals. The proposed filter in the wavelet packet domain then uses a priori knowledge of the target event and an estimate of noise features to selectively suppress the background noise. It is in fact a content-aware band-pass filter which can automatically pass the frequency bands that are more significant in the target than in the noise. Theoretical analysis shows that the proposed filtering method is capable of enhancing the target content while suppressing the background noise for signals with a low SNR. A condition to increase the probability of correct detection is also obtained. Experiments have been carried out on a large dataset of acoustic events that are contaminated by different types of environmental noise and white noise with varying SNRs. Results show that the proposed method is more robust and better adapted to noise than ordinary energy detectors, and it can work even with an SNR as low as -15 dB. A practical system for real time processing and multi-target detection is also proposed in this work

Target detection and classification using seismic signal processing in unattended ground sensor systems

This thesis studies the problem of target detection and classification in Unat-tended Ground Sensor (UGS) systems. One of the most challenging problems faced by target identification process is the design of a robust feature vector which is sta-ble and specific to a certain type of vehicle. UGS systems have been used to detect and classify a variety of vehicles. In these systems, acoustic and seismic signals are the most popularly used resources. This thesis studies recent development of target detection and classification techniques using seismic signals. Based on these studies, a new feature extraction algorithm. Spectral Statistics and Wavelet Coef-ficients Characterization (SSWCC), is proposed. This algorithm obtains a robust feature vector extracted from the spectrum, the power spectral density (BSD) and the wavelet coefficients of the signals. Shape statistics is used in both spectral and PSD analysis. These features not only describe the frequency distribution in the spectrum and PSD, but also shows the closeness of the magnitude of spectrum to the normal distribution. Furthermore, the wavelet coefficients are calculated to present the signal in the time-frequency domain. The energy and the distribution of the wavelet coefficients are used in feature extraction as well. After the features are obtained, principal component analysis (PGA) is used to reduce the dimension of the features and optimize the feature vector. Minimum-distance classifier and k-nearest neighbor (kNN) classifier are used to carry out the classification. Experimental results show that SSWCC provides a robust feature set for target identification. The overall performance level can reach as high as 90%

Moving Vehicle Recognition and Classification based on Time Domain Approach

AbstractDifferentially Hearing Ability Enabled (DHAE) community cannot discriminate the sound information from a moving vehicle approaching from their behind. This research work is mainly focused on recognition of different vehicles and its position using noise emanated from the vehicle A simple experimental protocol has been designed to record the sound signal emanated from the moving vehicle under different environment conditions and also at different vehicle speed Autoregressive modeling algorithm is used for the analysis to extract the features from the recorded vehicle noise signal. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation

High Accuracy Distributed Target Detection and Classification in Sensor Networks Based on Mobile Agent Framework

High-accuracy distributed information exploitation plays an important role in sensor networks. This dissertation describes a mobile-agent-based framework for target detection and classification in sensor networks. Specifically, we tackle the challenging problems of multiple- target detection, high-fidelity target classification, and unknown-target identification. In this dissertation, we present a progressive multiple-target detection approach to estimate the number of targets sequentially and implement it using a mobile-agent framework. To further improve the performance, we present a cluster-based distributed approach where the estimated results from different clusters are fused. Experimental results show that the distributed scheme with the Bayesian fusion method have better performance in the sense that they have the highest detection probability and the most stable performance. In addition, the progressive intra-cluster estimation can reduce data transmission by 83.22% and conserve energy by 81.64% compared to the centralized scheme. For collaborative target classification, we develop a general purpose multi-modality, multi-sensor fusion hierarchy for information integration in sensor networks. The hierarchy is com- posed of four levels of enabling algorithms: local signal processing, temporal fusion, multi-modality fusion, and multi-sensor fusion using a mobile-agent-based framework. The fusion hierarchy ensures fault tolerance and thus generates robust results. In the meanwhile, it also takes into account energy efficiency. Experimental results based on two field demos show constant improvement of classification accuracy over different levels of the hierarchy. Unknown target identification in sensor networks corresponds to the capability of detecting targets without any a priori information, and of modifying the knowledge base dynamically. In this dissertation, we present a collaborative method to solve this problem among multiple sensors. When applied to the military vehicles data set collected in a field demo, about 80% unknown target samples can be recognized correctly, while the known target classification ac- curacy stays above 95%