RI: Small: Binaural Sound Source Separation Robust to Listener Head Movements

该项目的目标是开发一种新的双耳模型，在复杂的环境中分离声音。该模型的新方面是，通过分析动态定位线索，并将这些线索与自身头部位置的信息相结合，可以利用头部运动来提高定位性能。此外，该模型还使用了双重方法来消除房间反射对声源定位和分离的影响。在第一阶段，使用基于自相关的算法消除镜面反射。在第二阶段，通过测量跨时间/频率区段的耳间互相关来消除弥散混响，知道这些值随着直接混响能量比的减小而减小。在开发模型的同时，还进行了一项行为研究，以更好地理解人类如何在复杂场景中稳健表现的基本原理。该项目旨在弥合从根本上了解听觉系统如何处理简单多声源场景的双耳任务，以及理解和建模当环境达到现实生活中的复杂性时它如何执行之间的差距。所得到的模型预计将实时运行，以定位机器人或监视应用中的声源，或用作声源分离算法、语音识别器、房间声学质量预测器和计算听觉场景分析(CASA)模型的前端。

The goal of this project is to develop a new binaural model to separate sounds in complex environments. The new aspect of the model is that it can utilize head movements to improve its localization performance by analyzing dynamic localization cues and combining these with information about its own head position. In addition, the model uses a dual approach to eliminate the influence of room reflections on sound source localization and segregation. In the first stage, specular reflections are eliminated using an autocorrelation- based algorithm. In the second stage, diffuse reverberation is removed by measuring interaural cross correlation across time/frequency bins, knowing that these values decrease with decreasing direct-to- reverberant energy ratio. The model development is accompanied by a behavioral study to better understand the underlying principles of how humans can perform robustly in complex scenarios. The results are also used as a benchmark test for the model algorithms.This project intends to bridge the gap that exists between fundamentally knowing how the auditory system processes binaural tasks for simple multiple-sound-source scenarios, and understanding and modeling how it performs when the environment reaches real-life complexity. The resulting model is expected to operate in real time to localize sound sources in robot or surveillance applications or serve as a front end for sound- source separation algorithms, speech recognizers, predictors for acoustical quality of rooms, and Computational Auditory Scene Analysis (CASA) models.