Using beamforming and binaural synthesis for the psychoacoustical evaluation of target sources in noise

Abstract

The potential of spherical-harmonics beamforming (SHB) techniques for the auralization of target sound sources in a background noise was investigated and contrasted with traditional head-related transfer function (HRTF)-based binaural synthesis. A scaling of SHB was theoretically derived to estimate the free-field pressure at the center of a spherical microphone array and verified by comparing simulated frequency response functions with directly measured ones. The results show that there is good agreement in the frequency range of interest. A listening experiment was conducted to evaluate the auralization method subjectively. A set of ten environmental and product sounds were processed for headphone presentation in three different ways: (1) binaural synthesis using dummy head measurements, (2) the same with background noise, and (3) SHB of the noisy condition in combination with binaural synthesis. Two levels of background noise (62, 72 dB SPL) were used and two independent groups of subjects (N=14) evaluated either the loudness or annoyance of the processed sounds. The results indicate that SHB almost entirely restored the loudness (or annoyance) of the target sounds to unmasked levels, even when presented with background noise, and thus may be a useful tool to psychoacoustically analyze composite sources.

Design theory for binaural synthesis: Combining microphone array recordings and head-related transfer function datasets

“…These modal representations enjoy popularity because they provide an encoding and decoding scheme of directional sound information at scalable resolutions [33,34,[45][46][47][48][49][50][51][52][53][54][55]. A directional resolution at a specific scale can be associated with a single parameter that is called the order, which is denoted by # in this section.…”

“…The entries of the matrix associated to the linear system are acoustic transfer functions from the positions of microphones to the positions used to obtain the HRTF dataset. Examples of binaural systems based on this approach are the SENZI system [35][36][37][38][39], the virtual artificial head (VAH) [41][42][43][44], binaural beamforming systems [34,45,47,49,52,53], and the implementations reported in [40,56].…”

Extension Method of Spherical Harmonics Beamforming for Sound Source Identification

“…小识别声源。声源在传声器阵列中心产生的声压通 常被定义为声压贡献 [5][6] ， 将其作为波束形成的输出 量来量化声源具有重要意义，不仅能为各声源贡献 量的排序评价提供依据，而且是进行声品质分析成 像、评价接受者主观感受的前提 [7] 。平面阵列延迟 求和波束形成已能够基于声压贡献识别声源 [5][6] 。 受自由场假设条件及传声器布置的限制，平面 阵列波束形成仅能识别阵列前方有限区域内的声 源 [8] 。相比而言，新型实心球阵列波束形成阵列旋 转对称性好、声场信息记录全面，阵列衍射作用强、 记录信号信噪比高，能够同时识别入射噪声和反射 噪声、实现任意三维声学环境的完整测量和全方位 声学成像，近年来，已被广泛应用于诸如汽车驾驶 室、飞机机舱等内场噪声源识别领域 [8][9][10][11][12][13] 。自 2002 年美国 MH Acoustics 的 JENS 等 [14] 给出实心球阵列 波束形成以来，该技术一直备受国内外学者关注， 至今仍方兴未艾。例如，2013 年，刘月婵等 [15] 研究 了高精度高分辨率的球阵列聚焦定位方法；2014 年， JIN 等 [16] 研究了多半径球阵列的优化设计方法； 同年，LEGG 等 [17] 还研究了三维聚焦声源面的自动 生成技术。球阵列采用球谐函数波束形成(Spherical harmonics beamforming, SHB)方法 [8][9][10][11] [8][9][10][11]14] ， 而真实识别对象是凹凸不平的复杂三维结构，这必 然使聚焦距离不完全等于各声源到阵列中心的真实 距离，揭示该现象对声压贡献计算的影响规律，对 实际应用具有重要指导意义。 事实上，任意复杂的实际声源都可以等效为由 若干个密集分布的点声源组合而成 [18][19] 。因此，本 文基于单极子点声源球面波假设， 推导出 SHB 在声 源位置输出量的理论表达式，在此基础上，引入声 压贡献修正系数，提出能够计算各声源声压贡献的 球谐函数波束形成扩展方法，进一步，基于单声源、 不相干声源、相干声源的数值仿真和试验验证所给 方法的正确性和有效性，研究聚焦距离不等于声源 到阵列中心的真实距离时该方法的准确性。 1 球阵列波束形成声压贡献计算理论…”

Spatial accuracy of binaural synthesis from rigid spherical microphone array recordings

“…During the last decade, there has been an increasing interest on methods for combining spatial information contained in HRTF datasets with recordings made with RSMAs [10][11][12][13][14][15][16][17][18][19][20]. In particular, the use of representations of HRTF datasets and RSMA recordings, in terms of solutions to the acoustic wave equation at different spatial resolutions (orders), enjoy popularity because they enable scalable encoding and simplify multichannel processing [10,11,[14][15][16]18,19].…”

“…In particular, the use of representations of HRTF datasets and RSMA recordings, in terms of solutions to the acoustic wave equation at different spatial resolutions (orders), enjoy popularity because they enable scalable encoding and simplify multichannel processing [10,11,[14][15][16]18,19].…”