多实例下Max和Noisy-Or池函数的比较 弱监督序列学习任务的学习

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摘要翻译：
许多序列学习任务要求对序列中的某些事件进行定位。由于获得指定事件开始和结束时间的强标记可能代价高昂，现代系统通常使用弱标记来训练，而没有明确的定时信息。多实例学习(MIL)是一种流行的弱标记学习框架。在MIL的一个常见场景中，需要选择一个池函数来聚合序列中各个步骤的预测。本文比较了语音识别任务和声音事件检测任务中的“max”和“noisy-or”池函数。我们发现最大池能够定位音素和声音事件，而嘈杂-或池失败。我们从理论上解释了两个池函数在序列学习任务上的不同行为。
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英文标题：
《Comparing the Max and Noisy-Or Pooling Functions in Multiple Instance
  Learning for Weakly Supervised Sequence Learning Tasks》
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作者：
Yun Wang, Juncheng Li, Florian Metze
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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：
  Many sequence learning tasks require the localization of certain events in sequences. Because it can be expensive to obtain strong labeling that specifies the starting and ending times of the events, modern systems are often trained with weak labeling without explicit timing information. Multiple instance learning (MIL) is a popular framework for learning from weak labeling. In a common scenario of MIL, it is necessary to choose a pooling function to aggregate the predictions for the individual steps of the sequences. In this paper, we compare the "max" and "noisy-or" pooling functions on a speech recognition task and a sound event detection task. We find that max pooling is able to localize phonemes and sound events, while noisy-or pooling fails. We provide a theoretical explanation of the different behavior of the two pooling functions on sequence learning tasks.
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PDF链接：
https://arxiv.org/pdf/1804.01146

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关键词：学习任务 NOI max localization Modification learning 能够 获得 事件 指定