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Echo delay estimation (EDE) serves as a preprocessing component within an acoustic echo canceller (AEC). Despite some progress over the past few decades, there is a dearth of literature on efficient algorithms. This paper introduces a binarized feature-matching (BFM) framework, encompassing a set of feature extraction methods, which are compared with traditional methods such as the GCC-Phat-based method, the adaptive-filter-based method, and popular methods published in WebRTC projects, in terms of both complexity and performance. The computational loads of the BFM methods are significantly lower, and a hybrid BFM method further enhances performance in terms of convergence speed and robustness. This method, characterized by its low complexity, benefits both traditional and NN-based AEC.