Sounds Source Segregation

声源隔离

• 批准号: 8501914
• 负责人: Robert A Lutfi
• 金额: $ 31.29万
• 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 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8501914
• 关键词: Accounting; Acoustics; Address; Adopted; Behavior; Complex; Cues; Data; Detection; Entropy; Environment; Equation; Evaluation; Frequencies; Funding; 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); Reliance; Research; Research Design; Residual state; Source; Speech; Staging; Testing; Uncertainty; Vision; Weight; Work; base; heuristics; model development; novel; novel strategies; preempt; programs; public health relevance; 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（由申请人提供）：我们拥有一种非凡的能力，能够“听出”（分离）构成我们在日常听力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当务之急，但这是一个巨大的挑战。扰动分析是一种方法论方法，它对视觉中的相关问题有着深入的理解[Murray，R.F. 2011. J. of Vision 11，1-25].这里的方法是适应试听。该应用程序进行了三个阶段：第一，简单的语音和环境的声音合成根据生成模型的声音产生源。第二，从非目标（噪声）源中分离目标的收听者决策策略是从决策权重（回归系数）确定的，决策权重将收听者关于目标的判断与声学参数中的合法扰动相关联，如生成模型所指示的。第三，通过将获得的权重和残差与基于生成源的运动方程优化分离的最大似然（ML）观测器的权重和残差进行比较来识别限制分离的因素。该方法是一个显着的进步，传统的方法，推断听众的决策策略，从性能的准确性或从放置在非法反对声线索的影响。具体目标是将该方法应用于（1）两大类分离模型之间的测试，目标增强与噪声消除，（2）关于源分离、共同命运与共同源的基于声学与基于力学的假设的测试预测，（3）量化目标噪声不确定性和相似性的相对影响，（4）确定水平优势对声源分离的影响，以及（5）通过使用决策权重来识别个体收听风格，来解释跨任务的不同行为模式。预计从这些研究中获得的知识将为声源隔离问题的更广泛方面的研究提供信息，包括与听力障碍的评估和治疗相关的研究，因为它影响了噪音环境中的日常听力。