Models for auditory perception, audio quality and speech intelligibility

虽然单耳和双耳听力的“有效”信号处理相对较好地被正常听力的数值模型所描述(听力受损的数值模型的描述程度较小)，但对于感知音频质量和语言可懂度的预测，几乎没有实际相关的知识转移的方法。对于双耳听力尤其如此，这是重要的，因为听力声学中的许多潜在应用是双耳的(例如，房间中的听力质量、听力辅助设备和工作场所的噪声暴露)。当前项目的目标是开发和评估基于单耳和双耳感知模型的音频质量和语音清晰度模型。在研究股的背景下，特别令人感兴趣的是，模型适合于考虑正常健康以及中到中度听力受损(个性化)的任意听(呈现)情景，处理呈现问题和个性化问题。对于各种信号失真的语音和音频质量是特别感兴趣的，因为一方面，具有非常高甚至透明特性的设备变得可用于家庭和移动应用，而另一方面，语音可理解性(例如，用于单通道降噪)的(有限)改善、收听努力和感知质量是算法改进的候选。本项目中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，这些模型将在主观听力测试的基础上应用于对其他项目的信号处理系统的评估，而其他项目提供的主观数据将用于进一步开发模型。

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.