摘要翻译：
这项工作的重点是对野外记录的鸟类发声进行可靠的检测和分割。鸟类声音的声学检测可用于多种鸟类分类群的自动监测和长期记录中感兴趣物种的查询。本文通过提出两种方法来解决这些问题：A）首先，将密度集应用于每周标记的数据，以推断数据集的注意力图（即。突出和凸轮）。我们通过将注意力地图引导到基于YOLO v2 DeepNet的检测框架来定位鸟类的发声，从而进一步推动了这一想法。B）深度自动编码器，即U-net，将鸟类发声的音频频谱图映射到其相应的二进制掩码，该掩码包围发声的频谱斑点，同时抑制其他音频源。我们只关注需要最少人参与的程序，适合扫描大量数据，以便分析它们，评估洞察力和假设，并识别鸟类活动的模式。希望这种方法将对需要设计生物多样性政策的研究人员、保护从业者和决策者有价值。
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英文标题：
《Deep Networks tag the location of bird vocalisations on audio
  spectrograms》
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作者：
Lefteris Fanioudakis, Ilyas Potamitis
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最新提交年份：
2017
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分类信息：

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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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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英文摘要：
  This work focuses on reliable detection and segmentation of bird vocalizations as recorded in the open field. Acoustic detection of avian sounds can be used for the automatized monitoring of multiple bird taxa and querying in long-term recordings for species of interest. These tasks are tackled in this work, by suggesting two approaches: A) First, DenseNets are applied to weekly labeled data to infer the attention map of the dataset (i.e. Salience and CAM). We push further this idea by directing attention maps to the YOLO v2 Deepnet-based, detection framework to localize bird vocalizations. B) A deep autoencoder, namely the U-net, maps the audio spectrogram of bird vocalizations to its corresponding binary mask that encircles the spectral blobs of vocalizations while suppressing other audio sources. We focus solely on procedures requiring minimum human attendance, suitable to scan massive volumes of data, in order to analyze them, evaluate insights and hypotheses and identify patterns of bird activity. Hopefully, this approach will be valuable to researchers, conservation practitioners, and decision makers that need to design policies on biodiversity issues.
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PDF链接：
https://arxiv.org/pdf/1711.04347