Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments

We address the problem of online localization and tracking of multiple moving speakers in reverberant environments. The paper has the following contributions. We use the direct-path relative transfer function (DP-RTF), an inter-channel feature that encodes acoustic information robust against reverberation, and we propose an online algorithm well suited for estimating DP-RTFs associated with moving audio sources. Another crucial ingredient of the proposed method is its ability to properly assign DP-RTFs to audio-source directions. Towards this goal, we adopt a maximum-likelihood formulation and we propose to use an exponentiated gradient (EG) to efficiently update source-direction estimates starting from their currently available values. The problem of multiple speaker tracking is computationally intractable because the number of possible associations between observed source directions and physical speakers grows exponentially with time. We adopt a Bayesian framework and we propose a variational approximation of the posterior filtering distribution associated with multiple speaker tracking, as well as an efficient variational expectation-maximization (VEM) solver. The proposed online localization and tracking method is thoroughly evaluated using two datasets that contain recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Persistent Structures in the Turbulent Boundary Layer

Persistent structures in the turbulent boundary layer are located and analyzed. The data are taken from flight experiments on large commercial aircraft. An interval correlation technique is introduced which is able to locate the structures. The Morlet continuous wavelet is shown to not only locates persistent structures but has the added benefit that the pressure data are decomposed in time and frequency. To better understand how power is apportioned among these structures, a discrete Coiflet wavelet is used to decompose the pressure data into orthogonal frequency bands. Results indicate that some structures persist a great deal longer in the TBL than would be expected. These structure contain significant power and may be a primary source of vibration energy in the airframe

Fault localization on power cables using time delay estimation of partial discharge signals

Precise localization of partial discharge (PD) sources on power cables is vital to prevent power line failures that can lead to significant economic losses for electrical suppliers. This study proposes four methods to estimate the time delay of PD signals under electromagnetic interference, including white Gaussian noise (WGN) and discrete sinusoidal interference (DSI), using denoised PD signals with signal-to-noise ratios ranging from 10.6 to -7.02 dB. The maximum peak detection (MPD) and cross-correlation (CC) approaches, as well as two new techniques, interpolation cross-correlation (ICC) and envelope cross-correlation (ECC), are evaluated for their effectiveness in PD source localization. The researchers employ the time difference of arrival (TDoA) algorithm to compute PD location using the double-end PD location algorithm, where the PD location precision serves as an indicator of the accuracy of the time delay estimation methods. The study concludes that CC and ICC are the most suitable methods for estimating the time delay of PD signals in the PD location algorithm, as they exhibit the lowest error rates. These results suggest that CC and ICC can be used effectively for precise PD source localization under electromagnetic interference on power cables

Sparse separation of sources in 3D soundscapes

A novel blind source separation algorithm applicable to extracting sources from within 3D soundscapes is presented. The algorithm is based on constructing a binary mask based on directional information. The validity of filtering using binary masked based on the ω-disjoint assumption is examined for several typical scenarios. Results for these test environments show an improvement by an order of magnitude when compared to similar work using speech mixtures. Also presented is the novel application of a dual-tree complex wavelet transform to sparse source separation, providing an alternative transformation to the short-time Fourier transform often used in this area. Results are presented showing compara- ble signal-to-interference performance, and significantly improved signal-to-distortion performance when compared against the short time Fourier transform. Results presented for the separation algorithm include quantitative measures of the separation performance for robust comparison against other separation algorithms. Consideration is given to the related problem of localising sources within 3D sound- scapes. Two novel methods are presented, the first using a peak estimation on a spherical histogram constructed using a geodesic grid, the second by adapting a self learning plastic self-organising map to operate on the surface of a unit sphere. It is concluded that the separation algorithm presented is effective for soundscapes comprising ecological or zoological sources. Specific areas for further work are recog- nised, both in terms of isolated technologies and towards the integration of this work into an instrument for soundscape recognition, evaluation and identification.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Detection Of Defects On Weld Bead Through The Wavelet Analysis Of The Acquired Arc Sound Signal

Recently, the development of online quality monitoring system based on the arc sound signal has become one of the main interests due its ability to provide the non-contact measurement. Notwithstanding, numerous unrelated-to-defect sources which influence the sound generation are one of the aspects that increase the difficulties of applying this method to detect the defect during welding process. This work aims to reveal the hidden information that associates with the existence of irregularities and porosity on the weld bead from the acquired arc sound by applying the discrete wavelet transform. To achieve the aim, the arc sound signal was captured during the metal inert gas (MIG) welding process of three API 5L X70 steel specimens. Prior to the signal acquisition process, the frequency range was set from 20 Hz to 10 000 Hz which is in audible range. In the next stage, a discrete wavelet transform was applied to the acquired sound in order to reveal the hidden information associated with the occurrence of discontinuity and porosity. According to the results, it was clear that the acquired arc sound was not giving an obvious indication of the presence of defect as well as its location due to the high noise level. More interesting findings have been obtained when the discrete wavelet transform (DWT) analysis was applied. The analysis results indicate that the level 8 of the approximate and detail wavelet coefficient have given a significant sign associated with the presence of irregularities and porosity respectively. Moreover, despite giving the information on the surfaces pores, the detail wavelet coefficient was found to give a clear indication of the sub-surface porosity formation during welding process. Hence, it could be concluded that the hidden information with respect to the occurrence of discontinuity and porosity on the weld bead could be obtained by applying the discrete wavelet transfor

Shooter localization and weapon classification with soldier-wearable networked sensors

The paper presents a wireless sensor network-based mobile countersniper system. A sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implements the acoustic detection and Time of Arrival (ToA) estimation algorithms on an FPGA. A 3-axis compass provides self orientation and Bluetooth is used for communication with the soldier’s PDA running the data fusion and the user interface. The heterogeneous sensor fusion algorithm can work with data from a single sensor or it can fuse ToA or Angle of Arrival (AoA) observations of muzzle blasts and ballistic shockwaves from multiple sensors. The system estimates the trajectory, the range, the caliber and the weapon type. The paper presents the system design and the results from an independent evaluation at the US Army Aberdeen Test Center. The system performance is characterized by 1-degree trajectory precision and over 95 % caliber estimation accuracy for all shots, and close to 100 % weapon estimation accuracy for 4 out of 6 guns tested