Microphone leakage or crosstalk is a common problem in multichannel close-talk audio recordings (e.g., meetings or live music performances), which occurs when a target signal does not only couple into its dedicated microphone, but also in all other microphone channels. For further signal processing such as automatic transcription of a meeting, a multichannel speaker interference reduction is required in order to eliminate the interfering speech signals in the microphone channels. The contribution of this paper is twofold: First, we consider multichannel close-talk recordings of a three-person meeting scenario with various different crosstalk levels. In order to eliminate the crosstalk in the target microphone channel, we extend a multichannel Wiener filter approach, which considers all individual microphone channels. Therefore, we integrate an adaptive filter method, which was originally proposed for acoustic echo cancellation (AEC), in order to obtain a well-performing interferer (noise) component estimation. This results in an improved speech-to-interferer ratio by up to 2.7 dB at constant or even better speech component quality. Second, since an AEC method requires typically clean reference channels, we investigate and report findings why the AEC algorithm is able to successfully estimate the interfering signals and the room impulse responses between the microphones of the interferer and the target speakers even though the reference signals are themselves disturbed by crosstalk in the considered meeting scenario.

中文翻译：

---

使用频域自适应滤波减少多声道扬声器干扰

麦克风泄漏或串扰是多通道近距离通话录音（例如会议或现场音乐表演）中的常见问题，当目标信号不仅耦合到其专用麦克风，而且还耦合到所有其他麦克风通道时，就会发生这种情况。对于进一步的信号处理，例如会议的自动转录，需要多通道扬声器干扰减少，以消除麦克风通道中的干扰语音信号。本文的贡献有两个：首先，我们考虑了具有各种不同串扰水平的三人会议场景的多通道近距离通话录音。为了消除目标麦克风通道中的串扰，我们扩展了多通道维纳滤波器方法，该方法考虑了所有单独的麦克风通道。所以，我们集成了最初为声学回声消除 (AEC) 提出的自适应滤波器方法，以获得性能良好的干扰（噪声）分量估计。这导致语音干扰比在恒定甚至更好的语音分量质量下提高了 2.7 dB。其次，由于 AEC 方法通常需要干净的参考通道，我们调查并报告了为什么 AEC 算法能够成功估计干扰信号以及干扰方麦克风与目标扬声器之间的房间脉冲响应的发现，即使参考信号是在考虑的会议场景中，他们自己受到串扰的干扰。以获得性能良好的干扰（噪声）分量估计。这导致语音干扰比在恒定甚至更好的语音分量质量下提高了 2.7 dB。其次，由于 AEC 方法通常需要干净的参考通道，我们调查并报告了为什么 AEC 算法能够成功估计干扰信号以及干扰方麦克风与目标扬声器之间的房间脉冲响应的发现，即使参考信号是在考虑的会议场景中，他们自己受到串扰的干扰。以获得性能良好的干扰（噪声）分量估计。这导致语音干扰比在恒定甚至更好的语音分量质量下提高了 2.7 dB。其次，由于 AEC 方法通常需要干净的参考通道，我们调查并报告了为什么 AEC 算法能够成功估计干扰信号以及干扰方麦克风与目标扬声器之间的房间脉冲响应的发现，即使参考信号是在考虑的会议场景中，他们自己受到串扰的干扰。