The Steered Response Power with Phase Transform (SRP-PHAT) is one of the leading methods for sound source localization because of its proven robustness in a noisy and reverberant environment. However, for the higher accuracy, the maximum power location needs to be searched over a dense grid, making the computational cost a real issue for large-scale microphone arrays. Modified SRP-PHAT introduced an effective strategy that computes the SRP functional at each discrete location, also considering the volume surrounding it. Subsequently, it explores the entire search space and achieves improved robustness over a coarse spatial grid. To accomplish higher spatial resolution with minimal computations, the Modified SRP-PHAT was further extended by employing an iterative grid decomposition search strategy. However, in challenging environments, the performance of the method deteriorates considerably with the iterative approach. In this paper, we suggest furtherance to iterative modified SRP-PHAT by employing adaptive search space in each iteration. The performance of the proposed method is extensively tested and compared to the state-of-the-art techniques on a simulated dataset and SMARD database to validate its reliability. The results exhibited that the proposed method achieves higher accuracy with only a marginal increase in computation expense than that of iterative modified SRP-PHAT.

带相位变换的转向响应功率 (SRP-PHAT) 是声源定位的主要方法之一，因为它在嘈杂和混响环境中的鲁棒性得到证实。然而，为了获得更高的精度，需要在密集网格上搜索最大功率位置，这使得计算成本成为大规模麦克风阵列的真正问题。修改后的 SRP-PHAT 引入了一种有效的策略，该策略计算每个离散位置的 SRP 函数，同时考虑其周围的体积。随后，它探索整个搜索空间，并在粗略的空间网格上实现了改进的鲁棒性。为了以最少的计算量实现更高的空间分辨率，修改后的 SRP-PHAT 通过采用迭代网格分解搜索策略进一步扩展。然而，在充满挑战的环境中，该方法的性能随着迭代方法而显着恶化。在本文中，我们建议通过在每次迭代中采用自适应搜索空间来进一步改进迭代修改的 SRP-PHAT。所提出方法的性能经过广泛测试，并与模拟数据集和 SMARD 数据库上的最新技术进行比较，以验证其可靠性。结果表明，与迭代修改的 SRP-PHAT 相比，所提出的方法实现了更高的精度，而计算费用仅略有增加。所提出方法的性能经过广泛测试，并与模拟数据集和 SMARD 数据库上的最新技术进行比较，以验证其可靠性。结果表明，与迭代修改的 SRP-PHAT 相比，所提出的方法实现了更高的精度，而计算费用仅略有增加。所提出方法的性能经过广泛测试，并与模拟数据集和 SMARD 数据库上的最新技术进行比较，以验证其可靠性。结果表明，与迭代修改的 SRP-PHAT 相比，所提出的方法实现了更高的精度，而计算费用仅略有增加。