Causality study on a feedforward active noise control headset with different noise coming directions in free field

A systematic analysis is proposed to predict the performance of a typical feedforward single channel ANC headset in terms of the delay, especially the non-causal delay caused by different noise coming directions. First, the performance of a non-causal feedforward system for a band-limited noise is analyzed by using a simplified pure delay model, where it is found that the noise reduction bandwidth is narrowed and the maximum noise reduction is decreased with the increase of the non-causal delay. Second, a systematic method is developed, which can be used to predict the system performance with measured primary and secondary path transfer functions in most practical sound fields and to study the effects of the control filter length and the path delay on the performance. Then, the causality of a typical feedforward active noise control headset with the primary source at 0 and 90 positions in an anechoic chamber is analyzed, and the performance for the two locations predicted by the systematic analysis is shown in good agreements with the experiment results. Finally, an experiment of a typical feedforward active noise control headset in a reverberation chamber is carried out, which shows the validity of the proposed systematic analysis for other more practical sound fields. © 2014 Elsevier Ltd. All rights reserved

Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithm

Analysis and design of filtered-x adaptive algorithms are conventionally done by assuming that the transfer function in the secondary path is a discrete-time system. However, in real systems such as active noise control, the secondary path is a continuous-time system. Therefore, such a system should be analyzed and designed as a hybrid system including discrete- and continuous- time systems and AD/DA devices. In this article, we propose a hybrid design taking account of continuous-time behavior of the secondary path via lifting (continuous-time polyphase decomposition) technique in sampled-data control theory

Active noise hybrid time-varying control for motorcycle helmets

Recent noise at work regulations in the EU (2003) have been established to prevent noise induced hearing loss (NIHL). This imposes better performance results to traditional feedback active noise control (ANC) in motorcycle helmets, which suffer from well known limitations. Here two new ideas are applied to this problem. First, an hybrid (feedforward/feedback) linear time invariant (LTI) controller is designed for a motorcycle helmet ANC, which improves the resulting attenuation. This is achieved by adding an extra pair of microphones which measure the external noise that is then used as the feedforward input signal. In addition and to increase even more the resulting performance, the air velocity is measured in real-time and used as the parameter which schedules a linear parameter varying (LPV) feedback (FB) controller. This is combined with the previous feedforward (FF) controller, resulting in a time-varying hybrid controller. Both hybrid, LTI and LPV controllers are designed using linear matrix inequality (LMI)-based optimization. Two experiments have been carried out to measure the relation between external noise spectra and velocity: a wind tunnel test and a freeway ride experience. The resulting controllers are tested in a simulation which uses actual data obtained from the freeway experiment. The resulting attenuations in this motivating study seem promising for future controller tests to be performed in real-time, with the adequate hardware.Fil: Castañé Selga, Rosa. Universitat Technical Zu Munich; AlemaniaFil: Sanchez Peña, Ricardo Salvador. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

A Frequency-Domain Method for Active Acoustic Cancellation of Known Audio Sources

Active noise control (ANC) is a real-time process in which a system measures an external, unwanted sound source and produces a canceling waveform. The cancellation is due to destructive interference by a perfect copy of the received signal phase-shifted by 180 degrees. Existing active noise control systems process the incoming and outgoing audio on a sample-by-sample basis, requiring a high-speed digital signal processor (DSP) and analog-to-digital converters (ADCs) with strict timing requirements on the order of tens of microseconds. These timing requirements determine the maximum sample rate and bit size as well as the maximum attenuation that the system can achieve. In traditional noise cancellation systems, the general assumption is that all unwanted sound is indeterminate. However, there are many instances in which an unwanted sound source is predictable, such as in the case of a song. This thesis presents a method for active acoustic cancellation of a known audio signal using the frequency characteristics of the known audio signal compared to that of a sampled, filtered excerpt of the same known audio signal. In this procedure, we must first correctly locate the sample index for which a measured audio excerpt begins via the cross-correlation function. Next, we obtain the frequency characteristics of both the known source (WAVE file of the song) and the measured unwanted audio by taking the Fast Fourier Transform (FFT) of each signal, and calculate the effective environmental transfer function (degradation function) by taking the ratio of the two complex frequency-domain results. Finally, we attempt to recreate the environmental audio from the known data and produce an inverted, synchronized, and amplitude-matched signal to cancel the audio via destructive interference. Throughout the process, we employ many signal conditioning methods such as FIR filtering, median filtering, windowing, and deconvolution. We illustrate this frequency-domain method in Native Instruments’ LabVIEW running on the Windows operating system, and discuss its reliability, areas for improvement, and potential future applications in mobile technologies. We show that under ideal conditions (unwanted sound is a known white noise source, and microphone, loudspeaker, and environmental filter frequency responses are all perfectly flat), we can achieve a theoretical maximum attenuation of approximately 300 dB. If we replace the white noise source with an actual song and the environmental filter with a low-order linear filter, then we can achieve maximum attenuation in the range of 50-70 dB. However, in a real-world environment, with additional noise and imperfect microphones, speakers, synchronization, and amplitude-matching, we can expect to see attenuation values in the range of 10-20 dB

Active Noise Control in The New Century: The Role and Prospect of Signal Processing

Since Paul Leug's 1933 patent application for a system for the active control of sound, the field of active noise control (ANC) has not flourished until the advent of digital signal processors forty years ago. Early theoretical advancements in digital signal processing and processors laid the groundwork for the phenomenal growth of the field, particularly over the past quarter-century. The widespread commercial success of ANC in aircraft cabins, automobile cabins, and headsets demonstrates the immeasurable public health and economic benefits of ANC. This article continues where Elliott and Nelson's 1993 Signal Processing Magazine article and Elliott's 1997 50th anniversary commentary~\cite{kahrs1997past} on ANC left off, tracing the technical developments and applications in ANC spurred by the seminal texts of Nelson and Elliott (1991), Kuo and Morgan (1996), Hansen and Snyder (1996), and Elliott (2001) since the turn of the century. This article focuses on technical developments pertaining to real-world implementations, such as improving algorithmic convergence, reducing system latency, and extending control to non-stationary and/or broadband noise, as well as the commercial transition challenges from analog to digital ANC systems. Finally, open issues and the future of ANC in the era of artificial intelligence are discussed.Comment: Inter-Noise 202

Hybrid wheelchair controller for handicapped and quadriplegic patients

In this dissertation, a hybrid wheelchair controller for handicapped and quadriplegic patient is proposed. The system has two sub-controllers which are the voice controller and the head tilt controller. The system aims to help quadriplegic, handicapped, elderly and paralyzed patients to control a robotic wheelchair using voice commands and head movements instead of a traditional joystick controller. The multi-input design makes the system more flexible to adapt to the available body signals. The low-cost design is taken into consideration as it allows more patients to use this system

Ultra-low power mixed-signal frontend for wearable EEGs

Electronics circuits are ubiquitous in daily life, aided by advancements in the chip design industry, leading to miniaturised solutions for typical day to day problems. One of the critical healthcare areas helped by this advancement in technology is electroencephalography (EEG). EEG is a non-invasive method of tracking a person's brain waves, and a crucial tool in several healthcare contexts, including epilepsy and sleep disorders. Current ambulatory EEG systems still suffer from limitations that affect their usability. Furthermore, many patients admitted to emergency departments (ED) for a neurological disorder like altered mental status or seizures, would remain undiagnosed hours to days after admission, which leads to an elevated rate of death compared to other conditions. Conducting a thorough EEG monitoring in early-stage could prevent further damage to the brain and avoid high mortality. But lack of portability and ease of access results in a long wait time for the prescribed patients. All real signals are analogue in nature, including brainwaves sensed by EEG systems. For converting the EEG signal into digital for further processing, a truly wearable EEG has to have an analogue mixed-signal front-end (AFE). This research aims to define the specifications for building a custom AFE for the EEG recording and use that to review the suitability of the architectures available in the literature. Another critical task is to provide new architectures that can meet the developed specifications for EEG monitoring and can be used in epilepsy diagnosis, sleep monitoring, drowsiness detection and depression study. The thesis starts with a preview on EEG technology and available methods of brainwaves recording. It further expands to design requirements for the AFE, with a discussion about critical issues that need resolving. Three new continuous-time capacitive feedback chopped amplifier designs are proposed. A novel calibration loop for setting the accurate value for a pseudo-resistor, which is a crucial block in the proposed topology, is also discussed. This pseudoresistor calibration loop achieved the resistor variation of under 8.25%. The thesis also presents a new design of a curvature corrected bandgap, as well as a novel DDA based fourth-order Sallen-Key filter. A modified sensor frontend architecture is then proposed, along with a detailed analysis of its implementation. Measurement results of the AFE are finally presented. The AFE consumed a total power of 3.2A (including ADC, amplifier, filter, and current generation circuitry) with the overall integrated input-referred noise of 0.87V-rms in the frequency band of 0.5-50Hz. Measurement results confirmed that only the proposed AFE achieved all defined specifications for the wearable EEG system with the smallest power consumption than state-of-art architectures that meet few but not all specifications. The AFE also achieved a CMRR of 131.62dB, which is higher than any studied architectures.Open Acces

Ambient Intelligence for Next-Generation AR

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.Comment: This is a preprint of a book chapter which will appear in the Springer Handbook of the Metavers