Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

We consider the problem of separating speech sources captured by multiple spatially separated devices, each of which has multiple microphones and samples its signals at a slightly different rate. Most asynchronous array processing methods rely on sample rate offset estimation and resampling, but these offsets can be difficult to estimate if the sources or microphones are moving. We propose a source separation method that does not require offset estimation or signal resampling. Instead, we divide the distributed array into several synchronous subarrays. All arrays are used jointly to estimate the time-varying signal statistics, and those statistics are used to design separate time-varying spatial filters in each array. We demonstrate the method for speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal Enhancement (IWAENC 2018

Acoustic Impulse Responses for Wearable Audio Devices

We present an open-access dataset of over 8000 acoustic impulse from 160 microphones spread across the body and affixed to wearable accessories. The data can be used to evaluate audio capture and array processing systems using wearable devices such as hearing aids, headphones, eyeglasses, jewelry, and clothing. We analyze the acoustic transfer functions of different parts of the body, measure the effects of clothing worn over microphones, compare measurements from a live human subject to those from a mannequin, and simulate the noise-reduction performance of several beamformers. The results suggest that arrays of microphones spread across the body are more effective than those confined to a single device.Comment: To appear at ICASSP 201

Smaller, Closer, Dirtier: Diesel Backup Generators in California

Quantifies the threat to air quality and human health by backup generators, and examines air quality in Los Angeles, San Diego, Sacramento, and Fresno, with some analysis of San Francisco as well

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Three path interference using nuclear magnetic resonance: a test of the consistency of Born's rule

The Born rule is at the foundation of quantum mechanics and transforms our classical way of understanding probabilities by predicting that interference occurs between pairs of independent paths of a single object. One consequence of the Born rule is that three way (or three paths) quantum interference does not exist. In order to test the consistency of the Born rule, we examine detection probabilities in three path intereference using an ensemble of spin-1/2 quantum registers in liquid state nuclear magnetic resonance (LSNMR). As a measure of the consistency, we evaluate the ratio of three way interference to two way interference. Our experiment bounded the ratio to the order of $10^{-3} \pm 10^{-3}$, and hence it is consistent with Born's rule.Comment: 11 pages, 4 figures; Improved presentation of figures 1 and 4, changes made in section 2 to better describe the experiment, minor changes throughout, and added several reference

Duality Symmetry in Kaluza-Klein n+D+dn+D+d Dimensional Cosmological Model

It is shown that, with the only exception of $n=2$, the Einstein-Hilbert action in $n+D+d$ dimensions, with $n$ times, is invariant under the duality transformation $a\to \frac{1}{a}$ and $b\to \frac{1}{b}$, where $a$ is a Friedmann-Robertson-Walker scale factor in $D$ dimensions and $b$ a Brans-Dicke scalar field in $d$ dimensions respectively. We investigate the $2+D+d$ dimensional cosmological model in some detail.Comment: 23 pages, Late

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper