Augmented Reality (AR) as a platform has the potential to facilitate the reduction of the cocktail party effect. Future AR headsets could potentially leverage information from an array of sensors spanning many different modalities. Training and testing signal processing and machine learning algorithms on tasks such as beam-forming and speech enhancement require high quality representative data. To the best of the author's knowledge, as of publication there are no available datasets that contain synchronized egocentric multi-channel audio and video with dynamic movement and conversations in a noisy environment. In this work, we describe, evaluate and release a dataset that contains over 5 hours of multi-modal data useful for training and testing algorithms for the application of improving conversations for an AR glasses wearer. We provide speech intelligibility, quality and signal-to-noise ratio improvement results for a baseline method and show improvements across all tested metrics. The dataset we are releasing contains AR glasses egocentric multi-channel microphone array audio, wide field-of-view RGB video, speech source pose, headset microphone audio, annotated voice activity, speech transcriptions, head bounding boxes, target of speech and source identification labels. We have created and are releasing this dataset to facilitate research in multi-modal AR solutions to the cocktail party problem.
翻译:作为平台的增强现实(AR)具有促进减少鸡尾酒效应的潜力。未来的AR头盔有可能利用一系列不同方式的传感器的信息。培训和测试信号处理和机器学习算法需要高质量的代表性数据。据作者所知,截至出版时,没有包含同步自利中心多声道的多声道和视频的数据集,在吵闹的环境中有动态的移动和交谈。在这项工作中,我们描述、评价和发布一个数据集,其中包含5小时多小时的多式数据,可用于培训和测试用于应用改进AR眼镜磨损器对话的多式数据。我们为基线方法提供语音智能、质量和信号到噪音比改进结果,并显示所有测试的衡量标准都有改进之处。我们发布的数据集包含AR镜中自利心型多声道的麦克风阵列音音、广域域RGB视频、语音源显示、头部麦克风声、附加说明的语音记录、语音记录、头套话语调箱、头套话语调比对质分析工具的改进。我们提供语言感知觉的语音和源识别数据。我们所创建的多式语音和标签标识标识的解决方案是用于解的。