Analysis, modeling and wide-area spatiotemporal control of low-frequency sound reproduction

This research aims to develop a low-frequency response control methodology capable of delivering a consistent spectral and temporal response over a wide listening area. Low-frequency room acoustics are naturally plagued by room-modes, a result of standing waves at frequencies with wavelengths that are integer multiples of one or more room dimension. The standing wave pattern is different for each modal frequency, causing a complicated sound field exhibiting a highly position-dependent frequency response. Enhanced systems are investigated with multiple degrees of freedom (independently-controllable sound radiating sources) to provide adequate low-frequency response control. The proposed solution, termed a chameleon subwoofer array or CSA, adopts the most advantageous aspects of existing room-mode correction methodologies while emphasizing efficiency and practicality. Multiple degrees of freedom are ideally achieved by employing what is designated a hybrid subwoofer, which provides four orthogonal degrees of freedom configured within a modest-sized enclosure. The CSA software algorithm integrates both objective and subjective measures to address listener preferences including the possibility of individual real-time control. CSAs and existing techniques are evaluated within a novel acoustical modeling system (FDTD simulation toolbox) developed to meet the requirements of this research. Extensive virtual development of CSAs has led to experimentation using a prototype hybrid subwoofer. The resulting performance is in line with the simulations, whereby variance across a wide listening area is reduced by over 50% with only four degrees of freedom. A supplemental novel correction algorithm addresses correction issues at select narrow frequency bands. These frequencies are filtered from the signal and replaced using virtual bass to maintain all aural information, a psychoacoustical effect giving the impression of low-frequency. Virtual bass is synthesized using an original hybrid approach combining two mainstream synthesis procedures while suppressing each method‟s inherent weaknesses. This algorithm is demonstrated to improve CSA output efficiency while maintaining acceptable subjective performance

A room acoustics measurement system using non-invasive microphone arrays

This thesis summarises research into adaptive room correction for small rooms and pre-recorded material, for example music of films. A measurement system to predict the sound at a remote location within a room, without a microphone at that location was investigated. This would allow the sound within a room to be adaptively manipulated to ensure that all listeners received optimum sound, therefore increasing their enjoyment. The solution presented used small microphone arrays, mounted on the room's walls. A unique geometry and processing system was designed, incorporating three processing stages, temporal, spatial and spectral. The temporal processing identifies individual reflection arrival times from the recorded data. Spatial processing estimates the angles of arrival of the reflections so that the three-dimensional coordinates of the reflections' origin can be calculated. The spectral processing then estimates the frequency response of the reflection. These estimates allow a mathematical model of the room to be calculated, based on the acoustic measurements made in the actual room. The model can then be used to predict the sound at different locations within the room. A simulated model of a room was produced to allow fast development of algorithms. Measurements in real rooms were then conducted and analysed to verify the theoretical models developed and to aid further development of the system. Results from these measurements and simulations, for each processing stage are presented

An Investigation of Bifurcation Acoustic Treatment Effects on Aft-Fan Engine Nacelle Noise

Increasing air traffic and more stringent aircraft noise regulations continue to expand requirements on aircraft noise reduction capabilities for conventional and unconventional aircraft configurations. A major component of the overall aircraft noise is the sound associated with the propulsion system mounted in the engine nacelle. Acoustic liners mounted in the aircraft engine nacelles provide a significant portion of the current fan noise reduction. However, they must be further optimized if challenging noise reduction goals are to be achieved. One area within the aft bypass duct that may be an excellent candidate for increased attention is the acoustic treatment on the engine bifurcations (i.e., engine pylon and lower bifurcation). This paper describes a fundamental study of the effects of bifurcation treatment on simulated aft fan noise, as well as the validation of numerical tools to predict such effects. Five bifurcation configurations (four treated and one hardwall) were fabricated and tested in the NASA Langley Curved Duct Test Rig. Results show that mode scattering may occur due to both the presence of the bifurcation, as well as variable impedance distributions on the bifurcation surface. Future work will also include optimization of bifurcation treatments for testing in the Curved Duct Test Rig. These initial results are promising and this work provides valuable information for further study and improvement of the performance of bifurcation acoustic treatments

非球形のアレイ形状を持つAmbisonicsの定式化

Tohoku University鈴木陽一課

Synthesis of Soundfields through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks

Most soundfield synthesis approaches deal with extensive and regular loudspeaker arrays, which are often not suitable for home audio systems, due to physical space constraints. In this article we propose a technique for soundfield synthesis through more easily deployable irregular loudspeaker arrays, i.e. where the spacing between loudspeakers is not constant, based on deep learning. The input are the driving signals obtained through a plane wave decomposition-based technique. While the considered driving signals are able to correctly reproduce the soundfield with a regular array, they show degraded performances when using irregular setups. Through a Convolutional Neural Network (CNN) we modify the driving signals in order to compensate the errors in the reproduction of the desired soundfield. Since no ground-truth driving signals are available for the compensated ones, we train the model by calculating the loss between the desired soundfield at a number of control points and the one obtained through the driving signals estimated by the network. Numerical results show better reproduction accuracy both with respect to the plane wave decomposition-based technique and the pressure-matching approach