Sound Source Localization for Robots (SSLR)

The Sound Source Localization for Robots (SSLR) Dataset is a collection of real robot audio recordings for the development and evaluation of sound source localization methods. Its features are: We recorded the audio with the Softbank's robot Pepper. The data include robot ego noise and overlapping speech sources. The dataset is separated into three sections: loudspeaker recordings, human talker recordings and noise recordings. It can serve as a standard benchmark dataset for the research of learning-based sound localization techniques. Reference If you use the data for your research or publication, please cite the following paper (bib file, fulltext): He, Weipeng, Petr Motlicek, and Jean-Marc Odobez. “Deep Neural Networks for Multiple Speaker Detection and Localization.” In 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018.

机器人声源定位（Sound Source Localization for Robots，简称SSLR）数据集是一组真实机器人采集的音频录音集合，用于声源定位方法的研发与评估。其特性如下：我们使用软银（Softbank）的Pepper机器人采集音频数据，数据集包含机器人自体噪声与重叠语音声源。该数据集分为三个部分：扬声器录音、人类讲话者录音以及噪声录音，可作为基于学习的声源定位技术研究的标准基准数据集。参考文献：若您将该数据集用于研究或发表工作，请引用如下论文（含BibTeX文件与全文）：何卫鹏、Petr Motlicek、Jean-Marc Odobez，《面向多说话人检测与定位的深度神经网络》，收录于2018年IEEE国际机器人与自动化会议（ICRA），2018年。

搜集汇总

背景与挑战

背景概述

SSLR数据集是一个用于机器人声源定位研究的真实音频记录集合，由Softbank的Pepper机器人录制，包含机器人自身噪声和重叠语音源，分为扬声器录音、人类讲话者录音和噪声录音三部分，旨在作为基于学习的声源定位技术的标准基准数据集，但访问受限仅用于非商业研究。