We present a novel Fast Direct Source Localization (FDSL) approach for acoustic isotropic sensor array applications. Unlike previous approximate maximum likelihood (AML) approach, the proposed FDSL focuses on the phase shift caused by time delays among sensors and obtains an analytical result without exhaustive search for all the possible locations. Using this phase shift model, conventional phase uncertainty problem can be solved by a light-weight linear search in the limited phase space, which greatly reduces the computational complexity of array processing. Theoretical analysis of the FDSL has been proposed and compared with the Cramér Rao Bound (CRB) of the AML approach. Simulations using real bird call data validate the advantage of proposed method.

We use the phase difference among sensors, by solving the phase wrapping problem based on the cross-spectrum between sensors, to obtain the direction of arrival without exhaustive search in the two-dimensional angle space. The proposed approach does not need the typical sensor separation or interfrequency separation requirements. Simulation results with narrowband, wideband signals (bird chirps), and field experiments using a Voxnet system validate the proposed method and demonstrate a significant performance enhancement at a low computation burden.

A bird phrase segmentation method using entropy-based change point detection is proposed. Spectrograms of bird calls are usually sparse while the background noise is relatively white. Therefore, considering the entropy of a sliding time-frequency block on the spectrogram, the entropy dips when detecting a signal and rises when the signal ends. Rather than applying a hard threshold on the entropy to determine the beginning and ending of a signal, a Bayesian change point detection is used to detect the statistical changes in the entropy sequence. Tests on a database of Cassin's Vireo (Vireo cassinii), our proposed segmentation method with spectral subtraction or a novel spectral whitening method as the front-end generates more accurate time labels, lower the false alarm rate than the conventional time-domain energy detection method and achieves high phrase classification rate.

In this paper, we present simulation and experimental studies of multiple bird source separation based on the Voxnet acoustic array node. The Approximate Maximum Likelihood (AML) method is used to estimate blindly the direction-of-arrivals(DOAs) of the sources to generate the steering vectors in order to separate the sources via beamforming. Simulation and measured data confirmed the proper operations of the AML beamforming algorithm and the Voxnet hardware node.

From the perspective of interoperability, Cognitive Radio (CR) is widely studied as an emerging alternative for public safety communications. Spectrum sensing is one of key functionalities enabling the opportunistic spectrum utilization for CR systems. The energy-based cooperative spectrum sensing is mostly adopted in current CR prototype systems due to the low complexity and high detection speed. The cooperative spectrum sensing performance is influenced by the number of collaborative CRs. However, the number of CRs is dynamically changing due to the dynamic behavior of public safety personnel. We study the dynamic behavior of CRs and also derive the exact analytical closed-form expressions for the sensing performance. These expressions lay the framework for utilizing the optimization tools to efficiently guarantee the global optimality for the system parameter design.

Cognitive Radio (CR) is widely studied as an emerging alternative for public safety communications from the aspect of opportunistic spectrum usage for interoperability, which is hampered by incompatible radio equipments and different operational radio frequency bands. Spectrum sensing is one of the key functionalities in Cognitive Radio Networks. Spatially distributed cooperative spectrum sensing can effectively solve the hidden primary user problem. However, the number of CRs in the cooperation is dynamically changing over the time at emergencies. We model the emergent behaviors of CRs and derive the dynamic probability of detection and probability of false alarm in the energy-based cooperative spectrum sensing for public safety cognitive radio networks over composite shadowing/fading channels.

Variation in the number of targets and sensors needs to be addressed in any realistic sensor system. Targets may come in or out of a region or may suddenly stop emitting detectable signal. Sensors can be subject to failure for many reasons. We derive a tracking algorithm with a model that includes these variations using random finite set theory (RFST). RFST is a generalization of standard probability theory into the finite set theory domain. This generalization does come with additional mathematical complexity. However, many of the manipulations in RSFT are similar in behavior and intuition to those of standard probability theory.

Diversity techniques for various communication and MIMO systems exploit the spatial and temporal diversity attributes to mitigate the ill effects of the fading channels. To evaluate these techniques, a method to generate multiple correlated fading channels is crucial. We propose a unified approach capable of generating correlated flat-fading envelope processes with the desired auto-correlation functions, cross-correlation functions, and probability density functions (pdfs). The proposed approach utilizes the Gaussian vector autoregressive process and the inverse transform sampling techniques. Comparing to the past research focusing on generating fading channels of the same family, the novelty of the proposed approach is its capability to generate fading processes of heterogeneous pdfs. Three examples are demonstrated. In the first example, the autocorrelated Nakagami channel is generated. The second example is designed to generate correlated 2x2 MIMO Rayleigh channels. In the third example, the proposed approach generates three correlated channels having Nakagami, Rician, and Rayleigh pdfs. The settings of the first two examples are adopted from previously published results, which are intended to verify the effectiveness of our proposed approach to tackle previously known problems. The third example demonstrates the novelty of the proposed approach to generate correlated channels of heterogeneous pdfs.

In a reverberant scenario, phase transformed weighted algorithms are more robust than Maximum Likelihood (ML) because of the insufficiency of the data model to incorporate reverberant information. This transformation has been applied to General Cross-Correlation and Steered Response Power algorithms; the latter has been shown to be more robust. For a multiple known number of sources, both algorithms have problems separating the sources that are close together because of the limitation caused by the resolution. Recently, an approach was made using simple well-known statistical room acoustics to model room reverberation, and another parametric approach called the Approximate Maximum Likelihood was made that was designed for multi-source estimations. By combining these methods, we developed an ML algorithm that is suitable for multi-source target estimates in a reverberant room.

This paper investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the deterministic and stochastic Cramer-Rao bound (CRB) and the deterministic maximum-likelihood (ML) DOA estimator under this model have been derived in M. Pesavento and A. Gershman, (July 2001), the stochastic ML DOA estimator under the same setting is still not available in the literature. In this paper, a new stochastic ML DOA estimator is derived. Its implementation is based on an iterative procedure which stepwise concentrates the log-likelihood function with respect to the signal and noise nuisance parameters. A modified inverse iteration algorithm is also presented for the estimation of the noise parameters. Simulation results have shown that the proposed algorithm is able to provide significant performance improvement over the conventional uniform ML estimator in nonuniform noise environments and require only a few iterations to converge to the nonuniform stochastic CRB.

This paper deals with the problem of estimating the directions-of-arrival (DOA) of multiple wideband sources using an array of sensors. While a variety of estimation techniques have been proposed in the literature, the maximum-likelihood (ML) DOA estimator has been shown to have superior performance under many challenging environments. In this paper, we propose a novel implementation for ML DOA estimator based on the cross-entropy (CE) method. Simulation results show the CE algorithm converges to the Cramer-Rao bound (CRB) in all scenarios within several iterations and the convergence speed is insensitive to the coherence of sources.

We propose a novel algorithm employing particle filters for acoustic source tracking in a reverberant environment. By incorporating the likelihood function computed through approximate maximum-likelihood (AML) method, the proposed algorithm is applicable to wideband sources and can be implemented for multiple sources tracking. Both computer simulation and experimental results show the effectiveness of the proposed algorithm

Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.

In this paper, we consider the use of a Compaq iPAQ 3760s, equipped with a built-in microphone and an external wireless card, for acoustic acquisition and processing to perform a distributed acoustical beamforming. Time synchronization among the microphones is achieved by the reference-broadcast synchronization method. Two beamforming algorithms, based on the time difference of arrivals (TDOA) among the microphones followed by a least-squares estimation, and the maximum-likelihood (ML) parameter estimation method, are used to perform source detection, enhancement, localization, delay-steered beamforming, and direction-of-arrival estimation. Experimental beamforming results using the iPAQs and the wireless network are reported.

Advances in microelectronics, array processing, and wireless networking have motivated the analysis and design of low-cost integrated sensing, computing, and communicating nodes capable of performing various demanding collaborative space-time processing tasks. In this paper, we consider the problem of coherent acoustic sensor array processing and localization on distributed wireless sensor networks. We first introduce some basic concepts of beamforming and localization for wide-band acoustic sources. A review of various known localization algorithms based on time-delay followed by least-squares estimations as well as the maximum-likelihood method is given. Issues related to practical implementation of coherent array processing, including the need for fine-grain time synchronization, are discussed. Then we describe the implementation of a Linux-based wireless networked acoustic sensor array testbed, utilizing commercially available iPAQs with built-in microphones, codecs, and microprocessors, plus wireless Ethernet cards, to perform acoustic source localization. Various field-measured results using two localization algorithms show the effectiveness of the proposed testbed. An extensive list of references related to this work is also included.

We consider a training based acoustic channel estimation technique for the purpose of audio dereverberation. A transmitting device that generates a low power (for minimal disturbance) controlled periodic pseudo-random binary probing signal is placed near the sound source, and the convolved probing and desired signals are collected. By a coherent integration of multiple periods of the received signal, a SINR enhanced training signal is obtained for channel estimation. Due to the periodic nature of the training signal, the channel can be efficiently estimated in the frequency-domain without any edge effects. The frequency-domain estimation dramatically reduces the computational load from matrix multiplications and inversions in the time-domain to that of scalar multiplications. Computer simulations and experimental results are presented to demonstrate the usefulness of this algorithm.

In this paper, we show several experimental acoustic source localization results using the maximum-likelihood parametric method. A direct localization is obtained when the sound source is placed inside the convex hull of the array. In the far-field case, we demonstrate the localization via the cross bearing from several widely separated arrays. In the case of two sources, an alternating projection procedure is applied to the ML method to estimate the two DOA's from the observed data. The ML algorithm is shown to be effective in locating sound sources of various types, e.g., vehicle, music, and even white noise.

Source localization using passive sensor arrays has been an active research problem for many years. Most near-field source localization algorithms involve two separate estimations, namely, relative time-delay estimations and source location estimation. A one-step maximum-likelihood parametric source localization algorithm is proposed based on the maximum correlation between time shifted sensor data at the true source location. The performance of the algorithm is evaluated and shown to approach the Cramer-Rao bound asymptotically in simulations.

This paper describes two new techniques for the joint estimation of source location and propagation speed using measured time difference of arrival (TDOA) for a sensor array. Previous methods for source location either assumed the array consisted of widely separated subarrays, or used an iterative procedure that required a good initial estimate. The first method directly estimates the source location and propagation speed by converting the solution of a system of nonlinear equations to an overdetermined system of linear equations with two supplemental variables. The second method provides improved estimates by using the solution of the first method as the initial condition for further iteration. The Cramer-Rao Bound (CRB) on the joint estimation is derived, and simulations show the new methods compare favorably to the bound.

We consider a digital signal processing sensor array system, based on randomly distributed sensor nodes, for surveillance and source localization applications. In most array processing the sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering.

Microphone array technology has been proposed for various audio, teleconference, hearing aid and voice recognition applications. By forming a focused beam toward the desired speech source, attenuating background noises and rejecting discrete spatial interferers, a microphone array can enhance the signal-to-noise-ratio (SNR) in a noisy environment with notable improvement in speech intelligibility. At the threshold of intelligibility, a one dB improvement in SNR can increase 10-15% speech intelligibility. It is also known that increasing SNR can result in significant improvement in the recognition rate of various automatic voice recognition systems. We propose a novel electronically steerable microphone array based on the maximum energy (ME) concentration criterion, which results in high SNR in rooms with reverberations and competing interferences. We present a prototype PC-based microphone array system designed for hearing aid applications but also applicable to other tasks.

Microphone array technology has been proposed for various audio, teleconference, and hearing aid applications. By forming a focused beam toward the desired speech source, attenuating background noises and rejecting discrete spatial interferers, a microphone array can enhance the SNR/SIR in a noisy environment with significant improvement in speech intelligibility. An array can also perform real time source-localization or direction-of-arrival (DOA) estimation in various applications. We present a high performance prototype PC-based microphone array system for hearing aid applications. Algorithms for maximum energy criterion array weight design needed in the speech processing mode as well as modified broadband near-field MUSIC schemes in the search mode are discussed. Then a PC-based microphone array system using a TMS320C40 DSP is described. Preliminary study of equalizing non-uniform response microphones is also discussed. Finally, some array performance results in free-space and reverberant room conditions are presented.