摘要翻译：
语音编码是语音通信的重要组成部分。其中一个重要的研究领域是特征提取，可以用来分析印度斯坦古典音乐。这方面的一个重要特征是基频，通常称为基音。在这项工作中，术语基音和它的声学感觉，频率是互换使用的。现有的许多基音检测算法可以检测给定乐曲中的主/基频，但我们提出了一种独特的基音检测算法，该算法使用了本文所述的binning方法，并使用了适当的bining大小。以前在这个主题上的工作为印度斯坦古典音乐的音高识别提供了线索。本文采用了基音级分布。它可以用来识别印度斯坦古典音乐中的音高，它是基于合适的语调和swaras。它遵循一种特定的比例模式，这是由托勒密提出并得到Zarlino证实的全音阶调谐。我们也给出了我们对这些比率的估计，并与上面的误差进行了比较。研究了该算法中由于改变bin大小而产生的误差，并提出了一个合适的bin大小的估计值，并对其进行了测试。因此，装箱算法有助于分离给定音乐作品中的重要音高。
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英文标题：
《Binning based algorithm for Pitch Detection in Hindustani Classical
  Music》
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作者：
Malvika Singh
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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：
  Speech coding forms a crucial element in speech communications. An important area concerning it lies in feature extraction which can be used for analyzing Hindustani Classical Music. An important feature in this respect is the fundamental frequency often referred to as the pitch. In this work, the terms pitch and its acoustical sensation, the frequency is used interchangeably. There exists numerous pitch detection algorithms which detect the main/ fundamental frequency in a given musical piece, but we have come up with a unique algorithm for pitch detection using the binning method as described in the paper using appropriate bin size. Previous work on this subject throws light on pitch identification for Hindustani Classical Music. Pitch Class Distribution has been employed in this work. It can be used to identify pitches in Hindustani Classical Music which is based on suitable intonations and swaras. It follows a particular ratio pattern which is a tuning for diatonic scale proposed by Ptolemy and confirmed by Zarlino is explored in this paper. We have also given our estimated of these ratios and compared the error with the above. The error produced by varying the bin size in our algorithm is investigated and an estimate for an appropriate bin size is suggested and tested. The binning algorithm thus helps to segregate the important pitches in a given musical piece.
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PDF链接：
https://arxiv.org/pdf/1801.02155