Models for auditory perception, audio quality and speech intelligibility

尽管正常听力的数值模型相对较好地描述了单耳和双耳听力的“有效”信号处理（听力受损程度较小），但只有很少的方法可以将实际相关的知识转移到感知音频质量和语音可理解性的预测中。这对于双耳听力尤其重要，因为许多听觉声学的潜在应用都是双耳的（例如，房间里的听力质量，听力支持设备，以及工作场所的噪音暴露）。当前项目的目标是基于单耳和双耳感知模型的音频质量和语音可理解性模型的开发和评估。在研究单位的背景下，特别感兴趣的是，模型适合于解释正常听力和中中度听力障碍（个性化）的任意听力（演示）场景，解决演示问题和个性化问题。各种各样的信号失真的语音和音频质量特别令人感兴趣，因为一方面，具有非常高甚至透明特性的设备可用于家庭和移动应用，另一方面，语音可理解性的（有限）改进(例如，对于单通道降噪，聆听努力和感知质量是算法改进的候选对象。本项目开发的模型将用于研究股的其他项目，以优化各自的信号处理算法。此外，在其他项目中，除了主观听力测试之外，还将使用这些模型来评估信号处理系统，而这里将使用其他项目提供的主观数据来进一步开发模型。

Although the "effective" signal processing for monaural and binaural hearing is relatively well described by numerical models for normal hearing (and to a lesser extent for hearing impaired), there are only few approaches for the practically relevant knowledge transfer to prediction of perceived audio quality and speech intelligibility. This is particularly the case for binaural hearing, which is of importance as many potential applications in hearing acoustics are binaural (e.g. listening quality in rooms, for hearing supportive devices, and for noise exposure at the work place). The goal of the current project is the development and evaluation of audio quality and speech intelligibility models which are based on monaural and binaural perception models. In the context of the Research Unit it is of particular interest that models are suited to account for arbitrary listening (presentation) scenarios for normal heanng as well as for miid-to-moderately hearing impaired (individualization), tackling the presentation problem and the individualization problem. Speech and audio quality for a wide variety of signal distortions are of particular interest as, on the one hand, devices with very high and even transparent characteristics become available for home and mobile applications, and on the other hand, next to (limited) improvement of speech intelligibility (e,g. for single channel noise reduction) listening effort and perceived quality are candidates for algorithm improvement. The models developed in this project will be used by the other projects of the Research Unit for optimization of the respective signal processing algorithms. Moreover, the models will be applied in addition to subjective listening tests for the evaluation of the signal processing systems in the other projects, while subjective data provided by the other projects will be used here to further develop the models.