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

A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Realization of an all-optical zero to π cross-phase modulation jump

We report on the experimental demonstration of an all-optical π cross-phase modulation jump. By performing a preselection, an optically induced unitary transformation, and then a postselection on the polarization degree of freedom, the phase of the output beam acquires either a zero or π phase shift (with no other possible values). The postselection results in optical loss in the output beam. An input state may be chosen near the resulting phase singularity, yielding a pi phase shift even for weak interaction strengths. The scheme is experimentally demonstrated using a coherently prepared dark state in a warm atomic cesium vapor

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