SIRCE: A Sensor Image Based Room-Centered Equalization System for Hearing Aids

摘要 混响的空间给听力受损的听众带来了严重的问题。混响 降低这类听众的声音质量和语音的可理解性，特别是如果他们 助听器(HA)用户。此外，混响还会降低许多其他方面的效果 有用的信号处理方法，例如语音增强算法，因为 混响引入了额外的虚拟信号源和背景噪声，从而增加了 这类算法必须解决声信号的复杂性问题。 我们提出了一种名为SIRCE的新方法，该方法可以对语音进行均衡(即消除混响) 以及复杂的真实世界声学环境中的其他音频信号 未知声源。SIRCE专为兼容和集成而设计 Into，NIH的开放语音平台倡议。 SIRCE的设计包括三个关键的创新特征：以房间为中心，传感器 以形象为基础，倾听者感知。以房间为中心意味着 系统嵌入在声学空间本身，而不是驻留在用户的耳朵( 助听器)。放置传感器(麦克风)和处理 房间中的组件而不是耳朵中的组件：降低成本、提高处理能力、轻松的外形 因素限制，实际部署两个以上麦克风，轻松共享 用户之间的处理能力和计算结果。以房间为中心的设计使 均衡系统的特殊意义，因为混响本身是特定于房间的。 基于传感器图像的意味着SIRCE计算每个活动的声像 每个传感器中的信号源。传感器图像提取(“六”)是我们对 盲源分离(BSS)的有源区。传感器图像提取决定了 即使在多个信号源的情况下，每个麦克风对每个信号源的响应也是孤立的 总是同时处于活动状态。Six计算每个声音的多个独立图像 信源(每个麦克风一个)，而典型的BSS算法仅生成单个 对每个来源的估计。这一点很重要，因为最有效的去混响方法 是多通道算法，需要从多个 麦克风。 侦听器感知意味着我们的系统使用来自侦听器的HA-INTERNAL的信号 麦克风、特定于听众的敏锐度配置文件和特定于听众的感兴趣来源 (“目标”)以确定该目标是否可被收听者听到；是否其他 声源是可听的；最佳处理策略和最佳传感器图像 呈现给听众。(当目标远离收听者并且靠近房间麦克风时， 收听医管局内部麦克风的反应通常不是最佳选择！) 在这个第一阶段的项目中，我们建议验证SIRCE计算、量化 它能够均衡混响语音，并估计语音的整体改善 可理解性SIRCE提供。 SIRCE系统将帮助助听器用户在复杂的情况下更好地理解语音 回响的空间。

Abstract Reverberant spaces create major problems for hearing impaired listeners. Reverberation reduces the sound quality and intelligibility of speech for such listeners, especially if they are hearing aid (HA) users. Moreover, reverberation reduces the effectiveness of many otherwise useful signal processing methods, such as speech enhancement algorithms, because reverberation introduces additional virtual sources and background noise that increase the complexity of the acoustic signals such algorithms must grapple with. We propose a novel method called SIRCE that “equalizes” (that is, dereverberates) speech and other audio signals in complex real-world acoustic environments containing multiple unknown acoustic sources. SIRCE has been designed for compatibility with, and integration into, NIH’s Open Speech Platform initiative. SIRCE’s design comprises three critical innovative features: it is room-centric, sensor image based, and listener aware. Room-centric means that important components of the system are embedded in the acoustic space itself, rather than residing in the user’s ear (the hearing aid). There are many advantages to placing sensors (microphones) and processing components in rooms rather than ears: reduced cost, increased processing power, relaxed form factor constraints, practical deployment of more than two microphones, and easy sharing of processing power and computational results between users. A room-centric design makes particular sense for an equalization system, because reverberation itself is room-specific. Sensor image based means that SIRCE calculates the acoustic image of each active source in each sensor. Sensor image extraction (“SIX”) is our innovative contribution to the active field of blind source separation (“BSS”). Sensor image extraction determines what the response of each microphone would be to each source in isolation even when multiple sources are always active simultaneously. SIX computes multiple independent images of each acoustic source (one for each microphone), whereas typical BSS algorithms only generate a single estimate for each source. This is important because the most effective dereverberation methods are multi-channel algorithms that require solo or source-separated inputs from multiple microphones. Listener aware means that our system employs the signals from the listener’s HA-internal microphones, a listener-specific acuity profile, and the listener-specified source of interest (“target”) to determine whether that target is acoustically audible to the listener; whether other sources are acoustically audible; and the optimal processing strategy and best sensor image to present to the listener. (When a target is far from the listener and close to a room microphone, listening to the HA internal mic response is often not the optimal choice!) In this Phase I project we propose to validate the sensor images SIRCE computes, quantify its ability to equalize reverberant speech, and estimate the overall improvement in speech intelligibility SIRCE delivers. The SIRCE system will help hearing aid users understand speech better in complex reverberant spaces.