Hear What I Want: an Acoustically Smart Personalized Common Room

听到我想要的：声学智能的个性化公共休息室

• 批准号: 10484661
• 负责人: RICHARD S GOLDHOR
• 金额: $ 25.96万
• 依托单位: SPEECH TECHNOLOGY/APPLIED RESEARCH CORP.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10484661
• 关键词: Acoustics; Adult; Algorithms; Architecture; Awareness; Bluetooth; Calibration; Cellular Phone; Code; Complex; Custom; Data; Devices; Ear; Effectiveness; Environment; Equilibrium; Healthcare; Hearing; Hearing Aids; Hearing problem; Image; Individual; Location; Masks; Measurement; Measures; Methods; Molds; Monitor; Names; Noise; Output; Pattern; Performance; Persons; Phase; Plug-in; Positioning Attribute; Procedures; Process; Property; Protocols documentation; Radiation; Reaction Time; Records; Reporting; Residual state; Signal Transduction; Source; Specific qualifier value; Speech; Speech Intelligibility; Speech Sound; Stream; Surveys; System; Testing; To specify; United States National Institutes of Health; Work; acoustic imaging; analog; base; design; digital; hearing impairment; improved; interest; microphone; novel; open source; preference; prototype; response; sensor; signal processing; sound; success; usability; wireless

Abstract Noisy rooms with multiple active sound sources create problems for hearing-impaired listeners. Unwanted masking sounds reduce the quality and intelligibility of speech that listeners want to hear, especially listeners with hearing deficits. We propose a novel assistive listening system called HWIW (“Hear What I Want”) that “scrubs” (i.e., removes) noise and other unwanted audio components from complex real-world environments containing multiple acoustic sources. HWIW has been designed for integration into NIH’s Open Speech Platform initiative for hearing aids and other personal audio devices. HWIW will leverage STAR Corp’s Multiple Algorithm Source Separation (MASS) application framework of “pluggable” acoustic separation signal processing modules. MASS is compatible with the Open Speech Platform and available on GitHub. HWIW is a room-centric system that delivers listener-specific audio to users through their smartphones. HWIW employs multiple microphones distributed around a room and connected to a room-specific dedicated server. An initial HWIW setup procedure is used to name permanently positioned “noisemakers” in the room such as speakers and appliances and characterize their acoustic radiation and reflection patterns. The HWIW Room Server processes audio signals from multiple HWIW mics to scrub the noisemaker-generated sounds from any microphone in the room a listener chooses to monitor. Multiple listeners are supported simultaneous- ly. Each listener uses a HWIW Listener App to specify which mic to monitor for sounds of interest and which of the known noisemakers to scrub. The HWIW Room Server computes an individualized scrubbed audio stream for each listener and transmits it wirelessly to their Listener App. The Listener App outputs this audio stream to the listener’s hearing aid, personal audio device, or earbuds as a standard line level or Bluetooth audio signal. HWIW is room-centric, sensor image-based, latency-optimized, and listener-aware. Important system components are embedded in the acoustic space itself, rather than in the user’s ear (the hearing aid). HWIW calculates the acoustic image of masking sounds in sensor response mixtures so that images of unwanted sounds can be removed. It computes the latency of its signal processing and balances the quality benefits of longer-latency scrubbing against the perceptual advantages of faster response times. HWIW employs listener- specific acuity profiles, information about the sound-isolating properties of each listener’s hearing aid or ear piece, and the listener-specified masking sounds to determine whether which maskers are audible given the listener’s acuity; and thus what the optimal noise scrubbing strategy is for that listener. In Phase I, we will implement three HWIW MASS scrubbing modules, and a prototype of the Listener App. We will objectively measure the ability of the scrubbing modules to scrub noise from microphone responses, calibrate those measurements against perceived residual noise, and evaluate Listener App useability. The HWIW system will help hearing impaired listeners hear what they want more clearly in noisy rooms.

摘要 有多个有源声源的嘈杂房间会给听力受损的听众带来问题。不想要 掩蔽声音降低了听众想要听到的语音的质量和可懂度，特别是听众 有听力缺陷的人我们提出了一种新的辅助听力系统，称为HWIW（“听到我想要的”）， “擦洗”（即，从复杂的真实世界环境中去除）噪声和其他不需要的音频成分 包含多个声源。HWIW被设计为集成到NIH的开放式演讲中 助听器和其他个人音频设备的平台倡议。HWIW将利用星星公司的多个 “可插拔”声分离信号的算法源分离（MASS）应用框架 处理模块。MASS与开放语音平台兼容，并可在GitHub上使用。 HWIW是一个以房间为中心的系统，通过智能手机向用户提供特定于手机的音频。 HWIW采用多个麦克风分布在房间周围，并连接到房间专用的麦克风。 server.初始HWIW设置程序用于命名房间中永久定位的“噪声制造者” 例如扬声器和电器，并表征它们的声辐射和反射模式。HWIW Room Server处理来自多个HWIW调音台的音频信号，以消除噪声发生器产生的声音 从听众选择监听的房间中的任何麦克风。同时支持多个监听器- ly.每个听众都使用HWIW麦克风应用程序来指定要监控哪个麦克风以获得感兴趣的声音， 已知的噪音制造者来清理HWIW房间服务器计算个性化的净化音频流 并将其无线传输到他们的音频应用程序。音频应用程序将此音频流输出到 助听器、个人音频设备或耳塞作为标准线路电平或蓝牙音频信号。 HWIW是以房间为中心的、基于传感器图像的、延迟优化的和可感知的。重要制度 助听器组件嵌入声学空间本身，而不是用户的耳朵（助听器）。HWIW 计算传感器响应混合物中掩蔽声音的声学图像， 声音是可以消除的。它计算其信号处理的延迟，并平衡 相对于更快的响应时间的感知优势，更长的延迟清理。HWIW雇佣了监听器- 特定的敏锐度配置文件，有关每个听众的助听器或耳朵的隔音性能的信息 作品，和特定的掩蔽声音，以确定哪些掩蔽声是可听的给定的 听众的敏锐度;以及因此对于该听众的最佳噪声擦洗策略是什么。 在第一阶段，我们将实施三个HWIW MASS清理模块，以及一个HWIW应用程序的原型。 我们将客观地测量净化模块从麦克风响应中净化噪声的能力， 根据感知的残余噪声校准这些测量值，并评估TMPApp的可用性。 HWIW系统将帮助听力受损的听众在嘈杂的房间里更清楚地听到他们想要的东西。