摘要翻译：
本文提出了一种基于深度残差神经网络的声源时域定位系统。来自3厘米间距的线性8通道麦克风阵列的数据被网络用于方向估计。提出利用深度残差网络进行声源定位，将定位任务视为分类任务。本文描述了收集的数据集和开发的神经网络体系结构。我们将在本研究中展示训练过程及其结果。所开发的系统在数据集的验证部分和新数据的实时捕获上进行了测试。30m秒声帧的分类准确率为99.2%。声源定位的标准偏差为4{\deg}。本文提出的声源定位方法在语音识别流水线上进行了测试。与使用GCC-PHAT声源定位的同类语音识别流水线相比，其误字率降低了1.14%。
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英文标题：
《Deep Residual Network for Sound Source Localization in the Time Domain》
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作者：
Dmitry Suvorov, Ge Dong and Roman Zhukov
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Sound        声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
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英文摘要：
  This study presents a system for sound source localization in time domain using a deep residual neural network. Data from the linear 8 channel microphone array with 3 cm spacing is used by the network for direction estimation. We propose to use the deep residual network for sound source localization considering the localization task as a classification task. This study describes the gathered dataset and developed architecture of the neural network. We will show the training process and its result in this study. The developed system was tested on validation part of the dataset and on new data capture in real time. The accuracy classification of 30 m sec sound frames is 99.2%. The standard deviation of sound source localization is 4{\deg}. The proposed method of sound source localization was tested inside of speech recognition pipeline. Its usage decreased word error rate by 1.14% in comparison with similar speech recognition pipeline using GCC-PHAT sound source localization.
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PDF链接：
https://arxiv.org/pdf/1808.06429