摘要翻译：
卡尔纳蒂克音乐是印度艺术音乐的一种形式，依靠口述传统在几代人之间传播知识。在过去的两百年里，使用规定的记谱法已经被用于学习、视唱和视唱。规定性符号为raga提供了通用的指导方针，不包括装饰或gamakas的信息，这些信息被认为是表征raga的关键。本文证明了规定记号包含raga属性，并能从一首卡尔纳蒂克音乐的八度折叠规定记号中可靠地识别出raga。我们将音符限制在7个音符，并抑制了更精细的音符位置信息。一种基于字典的方法捕获raga符号中重复音符模式的统计信息。所提出的从已知乐曲的raga符号中获得的重复音符模式（或简称SMRNP）的随机模型，在对应于相同乐曲的等效旋律数据上优于现有的基于旋律的raga识别技术。这反过来表明，对于Carnatic音乐来说，音符的过渡和运动在定义raga结构方面比确切的音符位置有更大的作用。
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英文标题：
《Raga Identification using Repetitive Note Patterns from prescriptive
  notations of Carnatic Music》
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作者：
Ranjani H. G. and T. V. Sreenivas
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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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英文摘要：
  Carnatic music, a form of Indian Art Music, has relied on an oral tradition for transferring knowledge across several generations. Over the last two hundred years, the use of prescriptive notations has been adopted for learning, sight-playing and sight-singing. Prescriptive notations offer generic guidelines for a raga rendition and do not include information about the ornamentations or the gamakas, which are considered to be critical for characterizing a raga. In this paper, we show that prescriptive notations contain raga attributes and can reliably identify a raga of Carnatic music from its octave-folded prescriptive notations. We restrict the notations to 7 notes and suppress the finer note position information. A dictionary based approach captures the statistics of repetitive note patterns within a raga notation. The proposed stochastic models of repetitive note patterns (or SMRNP in short) obtained from raga notations of known compositions, outperforms the state of the art melody based raga identification technique on an equivalent melodic data corresponding to the same compositions. This in turn shows that for Carnatic music, the note transitions and movements have a greater role in defining the raga structure than the exact note positions.
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PDF链接：
https://arxiv.org/pdf/1711.11357