ACES: A Product to Suppress or Enhance Critical Components in Acoustic Signals

描述(由申请人提供)： 常见的声学环境通常是来自多个声源的声音的复杂混合。其中一些来源包含听众需要理解的关键信息；另一些来源则是干扰听众理解的干扰因素。随着美国人口老龄化，很大一部分人很难应对如此复杂的声场，而且还在不断增长。目前的解决方案仅限于众所周知地放大所有声源的助听器，或者选择性地放大单个声源但被动地或主动地将听者与他或她的声学环境的其余部分隔离的耳机。我们建议开发一种名为ACES(声学组件增强系统)的产品来帮助听众，为他们提供从实际声学环境重建的虚拟声场，以这种方式，某些来源2称为可追踪源2被增强(如果听众想要关注他们)或被抑制(如果他们分心)。可追踪源是存在某种前声信息或踪迹的声源，其可用于识别和隔离声源的声音。为了分离声音，ACES将使用一个新的基于知识的组件，称为源假设生成器(SHG)。STAR已经确定了可以构建这样的SHG的重要的公共可追踪源类别。例如，扬声器发出的任何声音都是可追踪的。在这种重要的情况下，扬声器是声源，驱动扬声器的电信号是它的踪迹。如果可追踪源是信息源，则ACES会在ACES为收听者构造的虚拟声场中创建其增强版本。要做到这一点，ACES必须抑制原始的声学表示(可能会失真，难以理解)，并用更有利于听众的版本取而代之。ACE可以通过大声播放、时移、重复和放慢或加快播放速度来增强重建的声音。STAR拥有实施最先进的盲源分离算法的丰富经验，可以将独立的声源从多个麦克风信号的混合响应中分离出来。在第一阶段，我们计划以新的方式扩展这些算法，以利用由ACES声源假设代表的独立的基于知识的信息。实际上，我们将从物理麦克风对声场的响应中剔除可追踪来源的贡献。结果应该是，在一个需要更少麦克风的系统中，更好地分离可追踪和不可追踪的3隐藏来源，更好地满足用户的需求。我们还将进行一项感知实验，以测试在噪音环境中出现的口语单词的可理解性，当可追踪的语音源已如上所述得到增强时，或当其掩蔽噪声已使用ACES技术抑制时。我们预计，在第一阶段项目之后，将ACE与辅助听力设备和助听器集成将需要三到四年的进一步开发，并需要100-200万美元的支持资金。 公共卫生相关性： 拟议的项目支持ACES(声学组件增强系统)的开发，这是一种提高听力受损听众(包括许多上了年纪的婴儿潮一代)的生活质量的产品：这类听众难以处理复杂的声学环境，其中一些声源包含他们需要理解的关键信息，而其他来源是干扰理解的干扰因素。ACE使用新的过滤技术来抑制分散注意力的来源，并增强承载信息的来源(例如，公告)。ACES使用这些技术来生成由听众控制的虚拟现实，在这种虚拟现实中，干扰被抑制，重要的声音被增强。

DESCRIPTION (provided by applicant): Common acoustic environments are often complex mixtures of sounds from multiple acoustic sources. Some of these sources contain critical information listeners need to comprehend; others are distractions that interfere with listeners6 comprehension. As the US population ages, a significant and growing segment have difficulty coping with such complex sound fields. Current solutions are limited to hearing aids which notoriously amplify all acoustic sources, or headsets which selectively amplify a single source but passively or actively isolate the listener from the rest of his or her acoustic environment. We propose to develop a product called ACES (Acoustic Component Enhancement System) to help listeners by presenting them with a virtual sound field reconstructed from their actual acoustic environment in such a way that certain sources2called traceable sources2are enhanced (if they listener wants to attend to them) or suppressed (if they are distracting). Traceable sources are acoustic sources for which some kind of pre-acoustic information, or trace, exists that can be used to identify and isolate the sound of the source. To isolate the sound, ACES will use a novel knowledge-based component called a Source Hypothesis Generator (SHG). STAR has identified important classes of common traceable sources for which such SHGs can be constructed. For instance, any sound produced by a loudspeaker is traceable. In this important case, the speaker is the acoustic source, and the electrical signal that drives the speaker is its trace. If a traceable source is informative, ACES creates an enhanced version of it in the virtual sound field that ACES constructs for the listener. To do so, ACES must suppress the original acoustic representation (which may be distorted and difficult to comprehend) and replace it with a more 3listener-friendly4 version. ACES can enhance the reconstructed sound by playing it louder, time-shift it, repeat it, and play it slower or faster. STAR has extensive experience implementing state-of-the-art Blind Source Separation algorithms to separate independent acoustic sources from the mixed responses of multiple microphone signals. In Phase I, we plan to extend those algorithms in novel ways to leverage the independent knowledge-based information represented by the ACES acoustic source hypotheses. In effect, we will 3scrub4 the contributions of the traceable sources out of the physical microphone responses to the sound field. The result should be improved separation of both traceable and un-traceable 3hidden4 sources, in a system that requires fewer microphones, and meets users6 needs better. We will also carry out a perceptual experiment to test the intelligibility of spoken words presented in a noisy environment, when the traceable speech source has been enhanced as described above, or when its masking noises have been suppressed using the ACES technology. We anticipate that integrating ACES with assistive listening devices and hearing aids will require three or four years of further development beyond this Phase I project, and require $1-2 million in supporting funds. PUBLIC HEALTH RELEVANCE: The proposed project supports the development of ACES (Acoustic Component Enhancement System), a product to enhance the quality of life of hearing-impaired listeners, including many aging baby-boomers: such listeners have difficulty processing complex acoustic environments in which some acoustic sources contain critical information they need to comprehend, and other sources are distractions that interfere with comprehension. ACES uses novel filtering techniques to suppress distracting sources and enhance information-bearing ones (for example, announcements). ACES employs these techniques to generate a listener-controlled "virtual reality" in which distractions are suppressed and important sounds are enhanced.