FPGA-based architectures for acoustic beamforming with microphone arrays : trends, challenges and research opportunities

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed

An Overview of Lessons Learned from Sonic-boom Flight Research Projects Conducted by NASA Armstrong Flight Research Center

Over the course of four years, a team of aerospace engineers at the National Aeronautics and Space Administration Armstrong Flight Research Center completed four projects, each with the objective to research sonic-boom signatures from a ground-and building-level perspective. The relatively compressed timeline of these projects resulted in many lessons learned. With each successive project, these lessons have been more relied upon and referenced. This paper provides a high-level overview of the teams relevant lessons learned and the importance of these lessons for future projects

Aeroacoustic interactions of installed subsonic round jets

Additional noise sources are generated when an aircraft engine is mounted beneath a wing. The two main installation sources include: (1) reflection of the exhaust jet mixing noise from the underside of the wing, and (2) interaction between the turbulent jet plume and the trailing edge of the wing, or deployed flap. The strength, directivity and frequency content of these particular sources all serve to increase the time-averaged flyover aircraft noise level heard on the ground by residents beneath the flight path. As the bypass ratio and nacelle diameter of modern turbofan engines continues to increase, constraints on ground clearance are forcing under-wing-mounted engines to be coupled more closely to the wing and flap system, which, in turn, serves to accentuate both of these noise sources. Close-coupled nacelle-airframe designs are now a critical issue surrounding efforts to meet the future environmental targets for quieter civil aircraft.This research is principally aimed at understanding and predicting the groundpropagating noise generated by the latter of these two installed jet noise sources. In order to characterise the jet-surface interaction noise source, however, it is first necessary to isolate it. A small 1/50th model-scale acoustic experiment, therefore, is conducted in a semi-anechoic university laboratory using a single stream jet installed beneath a flat plate. Both far-field acoustic and near-field plate surface pressure data are measured to investigate the jet-surface interaction noise source. Results from this fundamental experiment are then used to help drive a larger, and more realistic, 1/10th modelscale test campaign, at QinetiQ's Noise Test Facility, where 3D wing geometry effects, Reynolds number scaling effects and static-to-flight effects are investigated. A jet-flap impingement tonal noise phenomenon is also identified and investigated at particularly closely-coupled jet-wing configurations. Finally, the first version of a fast, semi-empirical engineering tool is developed to predict the additional noise caused by jet-wing interaction noise, under static ambient flow conditions. It is hoped that this tool will serve to inform future commercial aircraft design decisions and, thus, will help to protect the acoustic environment of residents living beneath flight paths

Six Noise Type Military Sound Classifier

Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This negatively impacts the military's testing \& training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh previously developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Recent work will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and thunder. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives

Optimizing wide-area sound reproduction using a single subwoofer with dynamic signal decorrelation

A central goal in small room sound reproduction is achieving consistent sound energy distribution across a wide listening area. This is especially difficult at low-frequencies where room-modes result in highly position-dependent listening experiences. While numerous techniques for multiple-degree-of-freedom systems exist and have proven to be highly effective, this work focuses on achieving position-independent low-frequency listening experiences with a single subwoofer. The negative effects due to room-modes and comb-filtering are mitigated by applying a time-varying decorrelation method known as dynamic diffuse signal processing. Results indicate that spatial variance in magnitude response can be significantly reduced, although there is a sharp trade-off between the algorithm’s effectiveness and the resulting perceptual coloration of the audio signal.N/

The acoustics of concentric sources and receivers – human voice and hearing applications

One of the most common ways in which we experience environments acoustically is by listening to the reflections of our own voice in a space. By listening to our own voice we adjust its characteristics to suit the task and audience. This is of particular importance in critical voice tasks such as actors or singers on a stage with no additional electroacoustic or other amplification (e.g. in ear monitors, loudspeakers, etc.). Despite the usualness of this situation, there are very few acoustic measurements aimed to quantify it and even fewer that address the problem of having a source and receiver that are very closely located. The aim of this thesis is to introduce new measurement transducers and methods that quantify correctly this situation. This is achieved by analysing the characteristics of the human as a source, a receiver and their interaction in close proximity when placed in acoustical environments. The characteristics of the human voice and human ear are analysed in this thesis in a similar manner as a loudspeaker or microphone would be analysed. This provides the basis for further analysis by making them analogous to measurement transducers. These results are then used to explore the consequences of having a source and receiver very closely located using acoustic room simulation. Different techniques for processing data using directional transducers in real rooms are introduced. The majority of the data used in this thesis was obtained in rooms used for performance. The final chapters of this thesis include details of the design and construction of a concentric directional transducer, where an array of microphones and loudspeakers occupy the same structure. Finally, sample measurements with this transducer are presented

PREDICTION OF NOISE EMISSIONS USING PANEL CONTRIBUTION ANALYSIS SUPPLEMENTED WITH SCALE MODELING

Panel contribution analysis (PCA) can be used to predict machinery noise emissions, component contributions, and to assess the impact of sound reduction treatments. PCA is a measurement approach that is advantageous for complex machinery that is not easily modeled using conventional numerical analysis approaches. In this research, PCA is combined with scale modeling in order to speed up the necessary measurement work. Moreover, the method can be applied to much larger machinery and noise emissions can be assessed prior to locating and installing the equipment. This eliminates the necessity to use voluminous anechoic chambers. The machinery is first discretized into a collection of panels or patches. Volume velocities are measured for each patch with the machinery operating, and transfer functions are measured between panels and receiver locations with the machinery turned off. It is shown that transfer functions may be measured using a scale model. Then, the sound pressure level produced by the machinery is predicted. The method is first applied to a generator set and a 1/2 scale model is used to measure the acoustic transfer functions. It is demonstrated that PCA can be used to predict sound pressure levels in the far-field of a source even using a relatively small hemi-anechoic chamber. PCA was then used to assess the efficacy of barrier treatments. The PCA and scale modeling combination were then applied to an interior acoustics scenario. The acoustic emissions from three similar air handlers positioned throughout a bakery were predicted at two locations. Transfer functions were measured between the panels and three different customer locations using a 1/10th scale model. Transfer functions were corrected to account for air attenuation and predicted sound pressure levels compare well with measurement. The described approach may be used to determine the sound pressure levels in large interior spaces before they are constructed so long as volume velocities on the source can be measured a priori. In addition, strategies, such as barriers and sound absorption, to reduce the noise by modifications to the acoustic path were accurately assessed prior to equipment installation. PCA was then applied to a small unmanned aerial vehicle (UAV) and the sound pressure level was predicted 5.5 m away. In this case, both the panel volume velocities and sound pressures must be measured because the boundary encompassing the source is no longer semi-rigid. Measurements were performed on six measurement surfaces forming an imaginary box encompassing the UAV. A P-U Probe was utilized to measure both sound pressure and particle velocity on the imaginary surfaces. Acoustic transfer functions between the source and a receiver point were measured reciprocally. The noise level was predicted from measurements close to the UAV assuming both correlated and uncorrelated sources at the receiver point. The sound pressure level calculated by the correlated model compared well with direct measurement