In a multi-speaker scenario, a hearing aid lacks information on which speaker the user intends to attend, and therefore it often mistakenly treats the latter as noise while enhancing an interfering speaker. Recently, it has been shown that it is possible to decode the attended speaker from the brain activity, e.g., recorded by electroencephalography sensors. While numerous of these auditory attention decoding (AAD) algorithms appeared in the literature, their performance is generally evaluated in a non-uniform manner. Furthermore, AAD algorithms typically introduce a trade-off between the AAD accuracy and the time needed to make an AAD decision, which hampers an objective benchmarking as it remains unclear which point in each algorithm's trade-off space is the optimal one in a context of neuro-steered gain control. To this end, we present an interpretable performance metric to evaluate AAD algorithms, based on an adaptive gain control system, steered by AAD decisions. Such a system can be modeled as a Markov chain, from which the minimal expected switch duration (MESD) can be calculated and interpreted as the expected time required to switch the operation of the hearing aid after an attention switch of the user, thereby resolving the trade-off between AAD accuracy and decision time. Furthermore, we show that the MESD calculation provides an automatic and theoretically founded procedure to optimize the number of gain levels and decision time in an AAD-based adaptive gain control system.

中文翻译：

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可听的增益控制上下文中的听觉注意力解码算法的可解释性能指标。

在多扬声器场景中，助听器缺少用户打算参加哪个扬声器的信息，因此助听器经常在增强干扰扬声器的同时错误地将后者视为噪音。近来，已经显示出可以从例如由脑电图传感器记录的脑部活动中对出席的讲话者进行解码。尽管这些听觉解码（AAD）算法中有许多是在文献中出现的，但通常以不均匀的方式评估其性能。此外，AAD算法通常会在AAD精度和做出AAD决策所需的时间之间进行权衡，这阻碍了客观基准测试，因为尚不清楚每种算法的权衡空间中的哪一点在以下情况下是最佳的。神经控制的增益控制。为此，我们提出了一种可解释的性能指标，用于基于AAD决策指导的自适应增益控制系统来评估AAD算法。可以将这种系统建模为马尔可夫链，从中可以计算出最小期望切换持续时间（MESD），并将其解释为在用户进行注意力切换之后切换助听器操作所需的期望时间。在AAD准确性和决策时间之间进行权衡。此外，我们表明，MEAD计算提供了一种自动的，理论上可行的程序，可以优化基于AAD的自适应增益控制系统中的增益水平和决策时间。从中可以计算出最小预期切换持续时间（MESD），并将其解释为用户注意切换后切换助听器操作所需的预期时间，从而解决了AAD准确性和决策时间之间的折衷问题。此外，我们表明，MEAD计算提供了一种自动的，理论上可行的程序，可以优化基于AAD的自适应增益控制系统中的增益水平和决策时间。从中可以计算出最小预期切换持续时间（MESD），并将其解释为用户注意切换后切换助听器操作所需的预期时间，从而解决了AAD准确性和决策时间之间的折衷问题。此外，我们表明，MEAD计算提供了一种自动的，理论上可行的程序，可以优化基于AAD的自适应增益控制系统中的增益水平和决策时间。