Ultra-broadband local active noise control with remote acoustic sensing

Performance evaluation of an active headrest system using a filtered-x least mean square/fourth algorithm with virtual sensing

In an active headrest system virtual sensing tends to transfer the spatial zone of quiet from the residual error microphone to the ear canal. An attempt has been made in this paper to develop an auxiliary filter based virtual sensing scheme integrated with the filtered-x least mean square/fourth algorithm for an active headrest. The performance of the proposed method has been evaluated experimentally using periodic and band limited white noise. Improved noise control performance has been observed for both periodic and broadband noise. The effect of causality constraint of the performance of the algorithm has also been tested.

Spatially selective active noise control systems

Active noise control (ANC) systems are commonly designed to achieve maximal sound reduction regardless of the incident direction of the sound. When desired sound is present, the state-of-the-art methods add a separate system to reconstruct it. This can result in distortion and latency. In this work, we propose a multi-channel ANC system that only reduces sound from undesired directions, and the system truly preserves the desired sound instead of reproducing it. The proposed algorithm imposes a spatial constraint on the hybrid ANC cost function to achieve spatial selectivity. Based on a six-channel microphone array on a pair of augmented eyeglasses, results show that the system minimized only noise coming from undesired directions. The control performance could be maintained even when the array was heavily perturbed. The proposed algorithm was also compared with the existing methods in the literature. Not only did the proposed system provide better noise reduction, but it also required much less effort. The binaural localization cues did not need to be reconstructed since the system preserved the physical sound wave from the desired source.

Suboptimal controller design of global active noise control system for various acoustic environments

Conventional active noise control (ANC) systems in enclosed spaces are not easy to implement experimentally because they require a large number of microphones to measure sound pressure in global areas. Even if such systems are possible, if there are any changes in the locations of noise sources or surrounding objects, or if ANC system moves to another enclosed space, an expensive and time-consuming experimental calibration is again required. Implementation of global ANC in enclosed spaces is thus difficult. Therefore, we designed a global ANC system that can be used in various acoustic environments. The main idea involves suboptimal open-loop controller design in the free field. By using an open-loop controller, a controller calibrated once can be used in various acoustic environments. A controller designed in the free field derive a suboptimal solution without bias toward a specific acoustic environment. For controller design in the free field, we propose an experimental calibration approach in which the arrangement and the number of control speakers and microphones are determined by the frequency range and radiation pattern of the noise source. We conducted simulations and experiments to show that the designed controller in the free field is sufficiently effective in other enclosed spaces.

Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks

Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.

A study on coherence between virtual signal and physical signals in remote acoustic sensing

Remote acoustic sensing can be used to estimate the error signals in human ears without placing any physical microphones there. In this paper, the coherence between the signals picked up by physical microphones over a sphere surface and the signal obtained at the sphere center is investigated. Based on the multiple channel coherence formulas in the time domain and frequency domain, the relationship between the coherence and the placement of physical microphones is analyzed by numerical simulations first, then the experimental results obtained in a reverberation chamber and a car cabin are presented to verify the simulation results. Finally, a placement of physical microphones for active control of road noise in car cabins is discussed. Both the numerical and experimental results show that an upper limit frequency exists for accurate sound pressure estimation at the center of a sphere with the sound pressure on the sphere surface. For a sufficiently complex sound field such as that in a reverberation room or in a car, half the wavelength of the upper limit frequency is about the average distance among the physical microphones.

Active Noise Control for Aircraft Cabin Seats

In turboprop aircraft, the low-frequency noise field created by the propellers is the major contributor to the interior vibro-acoustic field, which determines a passenger’s discomfort. This paper deals with the experimental assessment of an active noise control (ANC) system for cabin seat headrests using two loudspeakers placed on both sides of the passenger’s head to create a local zone of quiet around the passenger’s ears. To deal with time-varying disturbances, the developed ANC system utilized a two-input-two-output filtered-X LMS algorithm developed in MATLAB/Simulink and implemented on a DSPACE control board to drive the secondary speakers and cancel the unwanted low-frequency noise components. The performance of the active headrest was investigated through real-time experimentation involving sensors, actuators, and electronic devices. The test results showed that up to approximatively 20 dB of sound attenuation could be realized in the passenger’s ears over a narrowband sound field replicated under laboratory conditions. Such achievements represent an excellent starting point toward practical applications in the design of more comfortable and acoustically efficient aircraft cabin seats.

Using a Retro-Reflective Membrane and Laser Doppler Vibrometer for Real-Time Remote Acoustic Sensing and Control

Microphones have been extensively studied for many decades and their related theories are well-established. However, the physical presence of the sensor itself limits its practicality in many sound field control applications. Laser Doppler vibrometers (LDVs) are commonly used for the remote measurement of surface vibration that are related to the sound field without the introduction of any such physical intervention. This paper investigates the performance and challenges of using a piece of retro-reflective film directly as an acoustic membrane pick-up with an LDV to sense its vibration to form a remote acoustic sensing apparatus. Due to the special properties of the retro-reflective material, the LDV beam can be projected to the target over a wide range of incident angles. Thus, the location of the LDV relative to the pick-up is not severely restricted. This is favourable in many acoustic sensing and control applications. Theoretical analysis and systematic experiments were conducted on the membrane to characterise its performance. One design has been selected for sensing sound pressure level above 20 dB and within the 200 Hz to 4 kHz frequency range. Two example applications-remote speech signal sensing/recording and an active noise control headrest-are presented to demonstrate the benefits of such a remote acoustic sensing apparatus with the retro-reflective material. Particularly, a significant 22.4 dB noise reduction ranging from 300 Hz to 6 kHz has been achieved using the demonstrated active control system. These results demonstrate the potential for such a solution with several key advantages in many applications over traditional microphones, primarily due to its minimal invasiveness.