Many modern smart devices are equipped with a microphone array and a loudspeaker (or are able to connect to one). Acoustic echo cancellation algorithms, specifically their multi-microphone variants, are essential components in such devices. On top of acoustic echos, other commonly encountered interference sources in telecommunication systems are reverberation, which may deteriorate the desired speech quality in acoustic enclosures, specifically if the speaker distance from the array is large, and noise. Although sub-optimal, the common practice in such scenarios is to treat each problem separately. In the current contribution, we address a unified statistical model to simultaneously tackle the three problems. Specifically, we propose a recursive EM (REM) algorithm for solving echo cancellation, dereverberation and noise reduction. The proposed approach is derived in the short-time Fourier transform (STFT) domain, with time-domain filtering approximated by the convolutive transfer function (CTF) model. In the E-step, a Kalman filter is applied to estimate the near-end speaker, based on the noisy and reveberant microphone signals and the echo reference signal. In the M-step, the model parameters, including the acoustic systems, are inferred. Experiments with human speakers were carried out to examine the performance in dynamic scenarios, including a walking speaker and a moving microphone array. The results demonstrate the efficiency of the echo canceller in adverse conditions together with a significant reduction in reverberation and noise. Moreover, the tracking capabilities of the proposed algorithm were shown to outperform baseline methods.

中文翻译：

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基于Kalman-EM方法的回声消除、去混响和降噪在线算法

许多现代智能设备都配备了麦克风阵列和扬声器（或能够与之连接）。声学回声消除算法，特别是它们的多麦克风变体，是此类设备中的重要组成部分。除了声学回声之外，电信系统中其他常见的干扰源是混响，这可能会降低隔音罩中所需的语音质量，特别是如果扬声器与阵列的距离很大，以及噪声。虽然不是最优的，但在这种情况下的常见做法是分别处理每个问题。在当前的贡献中，我们提出了一个统一的统计模型来同时解决这三个问题。具体来说，我们提出了一种用于解决回声消除、混响消除和降噪的递归电磁 (REM) 算法。所提出的方法是在短时傅立叶变换 (STFT) 域中导出的，时域滤波由卷积传递函数 (CTF) 模型近似。在 E 步骤中，基于噪声和混响麦克风信号以及回声参考信号，应用卡尔曼滤波器来估计近端扬声器。在 M 步中，推断模型参数，包括声学系统。对人类扬声器进行了实验，以检查动态场景中的性能，包括步行扬声器和移动麦克风阵列。结果证明了回声消除器在不利条件下的效率以及混响和噪声的显着降低。此外，所提出算法的跟踪能力被证明优于基线方法。