Natural signals are typically nonstationary. The complex-valued frequency spectra of nonstationary signals do not have zero spectral correlation, as is assumed for wide-sense stationary processes. Instead, these spectra have non-zero second-order noncircular statistics-that is, they are not rotationally invariant-that are potentially useful for detection, classification, and enhancement. These noncircular statistics are especially significant for transient events, which are common in many natural signals. In this paper we provide practical and effective estimators for spectral noncircularity and spectral correlation. We illustrate the behavior of our spectral noncircularity estimators for synthetic signals. Then, we derive a generalized likelihood ratio test using both circular and noncircular models and show how estimates of spectral noncircularity provide performance improvements for detection of natural acoustic events.

In this paper, we propose an innovative human tracking algorithm, which efficiently integrates the deformable part model (DPM) into the multiple-kernel based tracking using a moving camera. By representing each part model of a DPM detected human as a kernel, the proposed algorithm iteratively mean-shift the kernels (i.e., part models) based on color appearance and histogram of gradient (HOG) features. More specifically, the color appearance features, in terms of kernel histogram, are used for tracking each body part from one frame to the next, the deformation cost provided by DPM detector is further used to constrain the movement of each body kernel based on the HOG features. The proposed deformable multiple-kernel (DMK) tracking algorithm takes advantage of not only low computation owing to the kernel-based tracking, but also robustness of the DPM detector. Experimental results have shown the favorable performance of the proposed algorithm, which can successfully track human using a moving camera more accurately under different scenarios.

Many voice activity detection (VAD) systems use the magnitude of complex-valued spectral representations. However, using only the magnitude often does not fully characterize the statistical behavior of the complex values. We present two novel methods for performing VAD on single- and dual-channel audio that do completely account for the second-order statistical behavior of complex data. Our methods exploit the second-order noncircularity (also known as impropriety) of complex subbands of speech and noise. Since speech tends to be more improper than noise, higher impropriety suggests speech activity. Our single-channel method is blind in the sense that it is unsupervised and, unlike many VAD systems, does not rely on non-speech periods for noise parameter estimation. Our methods achieve improved performance over other state-of-the-art magnitude-based VADs on the QUT-NOISE-TIMIT corpus, which indicates that impropriety is a compelling new feature for voice activity detection.

This paper describes an improved detector for nonstationary harmonic signals. The performance improvement is accomplished by using a novel method for extending the coherence time of such signals. This method applies a transformation to a noisy signal that attempts to fit a simple model to the signal's slowly changing fundamental frequency over the analysis duration. By matching the change in the signal's fundamental frequency, analysis is more coherent with the signal over longer durations, which allows the use of longer windows and thus improves detection performance.

This paper proposes a robust driving recorder based on-road pedestrian tracking system, which effectively integrates Visual Simultaneous Localization And Mapping (V-SLAM), pedestrian detection, ground plane estimation, and kernel-based tracking techniques. The proposed system systematically detects the pedestrians from recorded video frames and tracks the pedestrians in the V-SLAM inferred 3-D space via a tracking-by-detection scheme. In order to efficiently associate the detected pedestrian frame-by-frame, we propose a novel tracking framework, combining the Constrained Multiple-Kernel (CMK) tracking and the estimated 3-D (depth) information, to globally optimize the data association between consecutive frames. By taking advantage of the appearance model and 3-D information, the proposed system not only achieves high effectiveness but also well handles occlusion in the tracking. Experimental results show the favorable performance of the proposed system which efficiently tracks on-road pedestrian in a moving camera equipped on a driving vehicle.

We introduce a novel speech enhancement algorithm for removing reverberation and noise from recorded speech data. Our approach centers around using a single-channel minimum mean-square error log-spectral amplitude (MMSE-LSA) estimator, which applies gain coefficients in a time-frequency domain to suppress noise and reverberation. The main contribution of this paper is that the enhancement is done in a time-frequency domain that is coherent with speech signals over longer analysis durations than the short-time Fourier transform (STFT) domain. This extended coherence is gained by using a linear model of fundamental frequency variation over the analysis frame. In the multichannel case, we preprocess the data with either a minimum variance distortionless response (MVDR) beamformer, or a delay-and-sum beamformer (DSB). We evaluate our algorithm on the REVERB challenge dataset. Compared to the same processing done in the STFT domain, our approach achieves significant improvement on the REVERB challenge objective metrics, and according to informal listening tests, results in fewer artifacts in the enhanced speech.

In this paper, we relax a commonly-used assumption about a class of nonstationary random processes composed of modulated wide-sense stationary random processes: that the fundamental frequency of the modulator is stationary within the analysis window. To compensate for the relaxation of this assumption, we define the generalized DEMON (%u201Cdemodulated noise%u201D) spectrum representing modulation frequency, which we use to increase the coherence time of such signals. Increased coherence time means longer analysis windows, which provides higher SNR estimators. We use the example of detection on both synthetic and real-world passive sonar signals to demonstrate this increase.

This paper presents an extension of Thomson's multitaper method for spectral estimation to estimate the spatial spectrum of an acoustic source in range and bearing. To begin, we solve the energy concentration problem in range and bearing for a linear array. This problem corresponds to finding the array shading (taper) whose array response is maximally concentrated in range and bearing. The Fresnel approximation for near-field sources is used to simplify the formulation of the maximization problem (making it equivalent to the Fractional Fourier Transform). This maximization reduces to an eigenvalue problem, similar to the case producing the prolate spheroidal wavefunctions. The resulting eigenvectors correspond to the optimal array shading. The array data is then beamformed with each eigenvector over the set of ranges and bearings of interest. The set of beamformer outputs are then combined to form a multitaper estimate of the spatial spectrum. This initial estimate is used to compute adaptive weights for each beamformer through an iterative least-squares minimization procedure, reducing sidelobe leakage. The adaptive weights are then used to form an adaptive multitaper estimate of the spatial spectrum in range and bearing space. Results of the method will be presented for simulated acoustic array data.

The analysis of signals consisting of multiple components with non-linear frequency modulation is required in a large number of applications, including the study of marine mammals vocalizations. This analysis has multiple motivations, such as investigating the impact of anthropogenic noise on marine mammal behavior, and species identification to avoid collision between ships and marine mammals. Such applications are normally conducted in a Passive Acoustics Monitoring (PAM) context, where there is low SNR together with very little a priori knowledge on the signals being analyzed. Recently, time-frequency tracking based on local analysis of the instantaneous phase has been successfully applied to underwater signals. In this paper, we present a robust version of this method, based on the use of the multiple analysis windows. The results provided for simulated and real signals demonstrate improved tracking of the instantaneous frequency in noise.

This paper presents an approach for adapting a tracking algorithm to the acoustic propagation environment. This adaptation is performed by incorporating the expected target signal-to-noise ratio (SNR) into the data association step through the measured contact amplitude. In this work, expected SNR is provided via acoustic modeling; estimates of bottom loss and scattering strength, required by the acoustic model, are obtained via inversion of the acoustic model based on measured multi-static sonar reverberation data. This paper shows that the use of distributed sensors provides improved estimates of the environmental parameters, and hence better estimates of the expected SNR.