Sounds Source Segregation

声源隔离

• 批准号: 8795705
• 负责人: Robert A Lutfi
• 金额: $ 30.98万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8795705
• 关键词: Accounting; Acoustics; Address; Adopted; Behavior; Complex; Cues; Data; Detection; Entropy; Environment; Equation; Evaluation; Frequencies; Funding; Health; Hearing; Hearing Aids; Hearing problem; Human; Individual; Individual Differences; Judgment; Knowledge; Measures; Mechanics; Methods; Modeling; Modification; Motion; Noise; Outcome Study; Pattern; Performance; Process; Property; Psychoacoustics; Rehabilitation therapy; Relative (related person); Research; Research Design; Residual state; Source; Speech; Staging; Testing; Uncertainty; Vision; Weight; Work; base; heuristics; model development; novel; novel strategies; preempt; programs; segregation; sound; technology development

DESCRIPTION (provided by applicant): We possess a remarkable ability to 'hear out' (segregate) the individual sound sources that makeup the complex, acoustic environments we encounter in everyday listening. Understanding this ability has been a preoccupation of psychoacoustic research, but it is a tremendous challenge. Perturbation analysis is a methodological approach that has advanced understanding of related problems in vision [Murray, R.F. 2011. J. of Vision 11, 1-25]. Here the approach is adapted to audition. The application proceeds in three stages: First, simple speech and environmental sounds are synthesized according to a generative model of the sound-producing source. Second, listener decision strategy in segregating a target from non-target (noise) source is determined from decision weights (regression coefficients) relating listener judgments regarding the target to lawful perturbations in acoustic parameters, as dictated by the generative model. Third, factors limiting segregation are identified by comparing the obtained weights and residuals to those of a maximum- likelihood (ML) observer that optimizes segregation based on the equations of motion of the generating source. The approach represents a significant advance over traditional methods that infer listener decision strategy from performance accuracy or from the effect of placing acoustic cues in unlawful opposition. Specific aims are to apply this method to (1) test between two major classes of segregation models, target enhancement vs. noise cancellation, (2) test predictions of acoustics-based versus mechanics-based hypotheses regarding source segregation, common fate vs. common source, (3) quantify the relative influence of target-noise uncertainty and similarity, (4) determine the influence of level dominance on sound source segregation, and (5) account for diverse patterns of behavior across tasks by using the decision weights to identify individual listening styles. It is expected that the knowledge gained from these studies will inform research on broader aspects of the problem of sound source segregation, including those relevant to the evaluation and treatment of disordered hearing as it impacts everyday listening in noisy environments.

描述(由申请者提供)：我们拥有非凡的能力，能够‘倾听’(隔离)构成我们在日常收听中遇到的复杂声学环境的各个声源。理解这种能力一直是心理声学研究的重点，但这是一个巨大的挑战。微扰分析是一种方法论方法，它促进了对视觉相关问题的理解[Murray，R.F.2011]。J.of Vision 11，1-25]。在这里，这种方法适用于海选。应用程序分三个阶段进行：首先，根据发音源的生成模型合成简单的语音和环境声音。第二，根据决策权重(回归系数)来确定将目标与非目标(噪声)源分离的听者决策策略，该决策权重将听者对目标的判断与由生成模型指示的声学参数中的合法扰动相关联。第三，通过将获得的权重和残差与最大似然(ML)观测器的权值和残差进行比较来识别限制分离的因素，该观测器基于产生源的运动方程来优化分离。与传统方法相比，该方法是一个显着的进步，传统方法根据性能准确性或从非法反对中放置声学提示的效果来推断听者的决策策略。该方法的具体目的是：(1)测试两大类分离模型，目标增强与噪声消除；(2)测试声学假设与基于力学假设的声源分离、共同命运与共同来源的预测；(3)量化目标噪声不确定性和相似性的相对影响；(4)确定声级优势对声源分离的影响；以及(5)通过使用决策权重来识别不同的收听风格，来解释不同任务的行为模式。预计从这些研究中获得的知识将有助于对声源隔离问题的更广泛方面的研究，包括与评估和治疗听力障碍有关的知识，因为它影响了嘈杂环境中的日常听力。