The paper presents an auditory distance perception model that is based on the extraction of statistical properties from monaural and binaural features in a reverberant room environment. The developed framework has considered both mono and stereo speech signals originated from different distances at various reverberation time periods. Hence, two models, namely single-channel monaural statistics and binaural-channel monaural statistics, have been discussed in this study. The distance-dependent statistical features from fused monaural coefficients, namely cepstral and envelope features, are chosen as an input to the different classification algorithms such as Gaussian mixture model-expectation maximization, support vector machine and random forest for the estimation of distance of a desired target user. The monaural coefficients are extracted in addition with the binaural cues, such as interaural time and level differences and interaural coherence (ITD, ILD and IC) for the binaural speech signals and eventually applied for the estimation of distance. The proposed monaural and binaural models observe an average of more than 5% better results compared to existing baseline techniques even at lower signal-to-noise ratio, 0 dB.

中文翻译：

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用于估计目标说话者距离的单耳和双耳统计特性分析

本文提出了一种听觉距离感知模型，该模型基于从混响房间环境中的单耳和双耳特征中提取统计特性。开发的框架考虑了来自不同距离、不同混响时间段的单声道和立体声语音信号。因此，本研究讨论了两种模型，即单声道单声道统计和双声道单声道统计。融合单声道系数的距离相关统计特征，即倒谱和包络特征，被选为不同分类算法的输入，如高斯混合模型-期望最大化、支持向量机和随机森林，用于估计所需的距离目标用户。除了双耳提示之外，还提取了单耳系数，例如双耳语音信号的双耳时间和电平差异以及双耳间相干性（ITD、ILD 和 IC），并最终应用于距离估计。与现有基线技术相比，即使在 0 dB 的较低信噪比下，所提出的单耳和双耳模型也观察到平均超过 5% 的结果。