The higher computational efficiency of the time difference of arrival (TDOA) based sound source localization makes it a preferred choice over steered response power (SRP) methods in real-time applications. However, unlike SRP, its implementation for multiple source localization (MSL) is not straight forward. It includes challenges as accurate feature extraction in unfavourable acoustic conditions, association ambiguity involved in mapping the feature extractions to the corresponding sources and complexity involved in solving the hyperbolic delay equation to estimate the source coordinates. Moreover, the dominating source and early reverberation make the detection of delay associated with the submissive sources further perplexing. Hence, this paper proposes a proficient three-step method for localizing multiple sources from delay estimates. In step 1, the search space region is partitioned into cubic subvolumes, and the delay bound associated with each one is computed. Hereafter, these subvolumes are grouped differently, such that whose associated TDOA bounds are enclosed by a specific delay interval, are clustered together. In step 2, initially, the delay segments and later each subvolume contained by the corresponding delay segment are traced for passing through estimated delay hyperbola. These traced volumes are updated by the weight to measure the likelihood of a source in it. The resultant generates the delay density map in the search space. In the final step, localization enhancement is carried out in the selected volumes using conventional SRP (C-SRP). The validation of the proposed approach is done by carrying out the experiments under different acoustic conditions on the synthesized data and, recordings from SMARD & Audio Visual 16.3 Corpus.

基于到达时间差（TDOA）的声源定位的更高计算效率使其成为实时应用中优于转向响应功率（SRP）方法的首选。但是，与SRP不同，它对多源本地化（MSL）的实现并非一帆风顺。它包括挑战，例如在不利的声学条件下进行准确的特征提取，将特征提取映射到相应源所涉及的关联模糊性以及解决双曲延迟方程以估算源坐标所涉及的复杂性。此外，主导源和早期混响使与顺从源相关的延迟检测更加令人困惑。因此，本文提出了一种从延迟估计中定位多个源的熟练的三步法。在步骤1中，将搜索空间区域划分为三次子体积，并计算与每个子体积关联的延迟范围。此后，将这些子卷进行不同的分组，以使其相关联的TDOA边界被特定的延迟间隔包围。在步骤2中，首先，跟踪延迟段以及随后由相应的延迟段包含的每个子体积，以通过估计的延迟双曲线。这些跟踪的体积将根据权重进行更新，以测量其中来源的可能性。结果将在搜索空间中生成延迟密度图。在最后一步中，使用常规SRP（C-SRP）在选定的卷中进行本地化增强。