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DYNAMIC RELATIVE TRANSFER FUNCTION ESTIMATION USING STRUCTURED SPARSE BAYESIAN LEARNING

机译:基于结构稀疏贝叶斯学习的动态相对传递函数估计

摘要

The use of a dynamic Relative Transfer Function (RTF) between two or more microphones may be used to improve multi-microphone speech processing applications. The dynamic RTF may improve speech intelligibility and speech quality in the presence of environmental changes, such as variations in head or body movements, variations in hearing device characteristics or wearing positions, or variations in room or environment acoustics. The use of an efficient and fast dynamic RTF estimation algorithm using short burst of noisy, reverberant mic recordings, which will be robust to head movements may provide more accurate RTFs which may lead to a significant performance increase.
机译:两个或多个麦克风之间的动态相对传递函数(RTF)的使用可用于改进多麦克风语音处理应用程序。动态RTF可以在存在环境变化的情况下改善语音清晰度和语音质量,所述环境变化例如是头部或身体运动的变化,听力装置特性或佩戴位置的变化,或者室内或环境声学的变化。使用有效的,快速的动态RTF估计算法,该方法使用短时的噪声,混响麦克风录音突发,对头部运动将很健壮,可以提供更准确的RTF,这可能会导致性能显着提高。

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