Development of a Technique for Restoring the Fidelity of Distorted Playback Audio Signal from Analog Cassette Tape

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkA simple yet elegant analog based technique for restoring the fidelity of playback audio signals emanating from magnetic cassette tapes is presented. The technique makes use of information from the high frequency bias signal in magnetic cassette tapes to correct for errors in the playback audio signal. Performance evaluation of the developed technique shows that the technique can correct for errors due to noise, scratches on the tape surface, clipping, and non-linear distortion. The developed technique will be valuable in restoring the fidelity of playback audio signal from magnetic cassette tapes stored in archives and private homes

Characterizing low-frequency artifacts during transcranial temporal interference stimulation (tTIS)

Transcranial alternating current stimulation (tACS) is a well-established brain stimulation technique to modulate human brain oscillations. However, due to the strong electro-magnetic artifacts induced by the stimulation current, the simultaneous measurement of tACS effects during neurophysiological recordings in humans is challenging. Recently, transcranial temporal interference stimulation (tTIS) has been introduced to stimulate neurons at depth non-invasively. During tTIS, two high-frequency sine waves are applied, that interfere inside the brain, resulting in amplitude modulated waveforms at the target frequency. Given appropriate hardware, we show that neurophysiological data during tTIS may be acquired without stimulation artifacts at low-frequencies. However, data must be inspected carefully for possible low-frequency artifacts. Our results may help to design experimental setups to record brain activity during tTIS, which may foster our understanding of its underlying mechanisms.</p

Codec Data Augmentation for Time-domain Heart Sound Classification

Heart auscultations are a low-cost and effective way of detecting valvular heart diseases early, which can save lives. Nevertheless, it has been difficult to scale this screening method since the effectiveness of auscultations is dependent on the skill of doctors. As such, there has been increasing research interest in the automatic classification of heart sounds using deep learning algorithms. However, it is currently difficult to develop good heart sound classification models due to the limited data available for training. In this work, we propose a simple time domain approach, to the heart sound classification problem with a base classification error rate of 0.8 and show that augmentation of the data through codec simulation can improve the classification error rate to 0.2. With data augmentation, our approach outperforms the existing time-domain CNN-BiLSTM baseline model. Critically, our experiments show that codec data augmentation is effective in getting around the data limitation.Comment: Accepted by ICAICTA 202

Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter

Evoked potentials (EPs) are time-varying signals typically buried in relatively large background noise. To extract the EP more effectively from noise, we had previously developed an approach using an adaptive signal enhancer (ASE) (Chen et al., 1995). ASE requires a proper reference input signal for its optimal performance. Ensemble- and moving window-averages were formerly used with good results. In this paper, we present a new method to provide even more effective reference inputs for the ASE. Specifically, a Gaussian radial basis function neural network (RBFNN) was used to preprocess raw EP signals before serving as the reference input. Since the RBFNN has built-in nonlinear activation functions that enable it to closely fit any function mapping, the output of RBFNN can effectively track the signal variations of EP. Results confirmed the superior performance of ASE with RBFNN over the previous method.published_or_final_versio

Sonic Booms in Atmospheric Turbulence (SonicBAT): The Influence of Turbulence on Shaped Sonic Booms

The objectives of the Sonic Booms in Atmospheric Turbulence (SonicBAT) Program were to develop and validate, via research flight experiments under a range of realistic atmospheric conditions, one numeric turbulence model research code and one classic turbulence model research code using traditional N-wave booms in the presence of atmospheric turbulence, and to apply these models to assess the effects of turbulence on the levels of shaped sonic booms predicted from low boom aircraft designs. The SonicBAT program has successfully investigated sonic boom turbulence effects through the execution of flight experiments at two NASA centers, Armstrong Flight Research Center (AFRC) and Kennedy Space Center (KSC), collecting a comprehensive set of acoustic and atmospheric turbulence data that were used to validate the numeric and classic turbulence models developed. The validated codes were incorporated into the PCBoom sonic boom prediction software and used to estimate the effect of turbulence on the levels of shaped sonic booms associated with several low boom aircraft designs. The SonicBAT program was a four year effort that consisted of turbulence model development and refinement throughout the entire period as well as extensive flight test planning that culminated with the two research flight tests being conducted in the second and third years of the program. The SonicBAT team, led by Wyle, includes partners from the Pennsylvania State University, Lockheed Martin, Gulfstream Aerospace, Boeing, Eagle Aeronautics, Technical & Business Systems, and the Laboratory of Fluid Mechanics and Acoustics (France). A number of collaborators, including the Japan Aerospace Exploration Agency, also participated by supporting the experiments with human and equipment resources at their own expense. Three NASA centers, AFRC, Langley Research Center (LaRC), and KSC were essential to the planning and conduct of the experiments. The experiments involved precision flight of either an F-18A or F-18B executing steady, level passes at supersonic airspeeds in a turbulent atmosphere to create sonic boom signatures that had been distorted by turbulence. The flights spanned a range of atmospheric turbulence conditions at NASA Armstrong and Kennedy in order to provide a variety of conditions for code validations. The SonicBAT experiments at both sites were designed to capture simultaneous F-18A or F-18B onboard flight instrumentation data, high fidelity ground based and airborne acoustic data, surface and upper air meteorological data, and additional meteorological data from ultrasonic anemometers and SODARs to determine the local atmospheric turbulence and boundary layer height

A systemic approach to the preservation of audio documents: methodology and software tools

This paper presents a methodology for the preservation of audio documents, the operational protocol that acts as the methodology, and an original open source software system that supports and automatizes several tasks along the process. The methodology is presented in the light of the ethical debate that has been challenging the international archival community for the last thirty years. The operational protocol reflects the methodological principles adopted by the authors, and its effectiveness is based on the results obtained in recent research projects involving some of the finest audio archives in Europe. Some recommendations are given for the rerecording process, aimed at minimizing the information loss and at quantifying the unintentional alterations introduced by the technical equipment. Finally, the paper introduces an original software system that guides and supports the preservation staff along the process, reducing the processing timing, automatizing tasks, minimizing errors, and using information hiding strategies to ease the cognitive load. Currently the software system is in use in several international archives

Analysis and correction of the helium speech effect by autoregressive signal processing

SIGLELD:D48902/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Application of Signal Advance Technology to Electrophysiology

Medical instrumentation used in diagnosis and treatment relies on the accurate detection and processing of various physiological events and signals. While signal detection technology has improved greatly in recent years, there remain inherent delays in signal detection/ processing. These delays may have significant negative clinical consequences during various pathophysiological events. Reducing or eliminating such delays would increase the ability to provide successful early intervention in certain disorders thereby increasing the efficacy of treatment. In recent years, a physical phenomenon referred to as Negative Group Delay (NGD), demonstrated in simple electronic circuits, has been shown to temporally advance the detection of analog waveforms. Specifically, the output is temporally advanced relative to the input, as the time delay through the circuit is negative. The circuit output precedes the complete detection of the input signal. This process is referred to as signal advance (SA) detection. An SA circuit model incorporating NGD was designed, developed and tested. It imparts a constant temporal signal advance over a pre-specified spectral range in which the output is almost identical to the input signal (i.e., it has minimal distortion). Certain human patho-electrophysiological events are good candidates for the application of temporally-advanced waveform detection. SA technology has potential in early arrhythmia and epileptic seizure detection and intervention. Demonstrating reliable and consistent temporally advanced detection of electrophysiological waveforms may enable intervention with a pathological event (much) earlier than previously possible. SA detection could also be used to improve the performance of neural computer interfaces, neurotherapy applications, radiation therapy and imaging. In this study, the performance of a single-stage SA circuit model on a variety of constructed input signals, and human ECGs is investigated. The data obtained is used to quantify and characterize the temporal advances and circuit gain, as well as distortions in the output waveforms relative to their inputs. This project combines elements of physics, engineering, signal processing, statistics and electrophysiology. Its success has important consequences for the development of novel interventional methodologies in cardiology and neurophysiology as well as significant potential in a broader range of both biomedical and non-biomedical areas of application

The Effects of Geomagnetic Disturbances on Electrical Power Systems

Solar storms that generate coronal mass ejections are a cause for concern due to the damage that they cause in high voltage power grids. Geomagnetically induced currents can be introduced onto the grid and cause many adverse effects. The vulnerability of the bulk electric power systems to such events has increased during the past few decades because the power system transmission lines have become more interconnected and have increased in length. Real and reactive power flows, voltage fluctuations, frequency shifts, undesired relay operations, higher order harmonic currents, undesired damage to assets and failure of assets are all possible outcomes from a large geomagnetic disturbance. A 100 year solar storm could cause mass blackouts and colossal damage to any high voltage power grid, if proper monitoring and mitigation techniques are not used. This thesis presents an in-depth background on geomagnetic disturbances and how they affect the electrical power grid. The thesis will model geomagnetic disturbances on a theoretical grid using the simulation software OpenDSS. The thesis will also discuss monitoring and mitigation techniques that can be applied to the power grid to lessen the chance of failure or damage to assets, and analyze real world data collected from a Midwestern solar storm that had an effect on two power transformers equipped with online monitoring