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摘要翻译：
从单音旋律生成和弦进行是一个具有挑战性的问题，因为和弦进行需要一系列分层的音符同时演奏。本文提出了一种新的从符号旋律中生成和弦序列的方法，该方法使用在导音表数据库上训练的双向长短时记忆(BLSTM)网络。为此，从单音旋律的每小节中的音符中提取了一组由12个半音组成的特征向量。为了保证数据共享统一的密钥和持续时间特征，对向量的密钥和时间签名进行了归一化。然后，BLSTM网络从数据中学习以合并时间依赖性以产生和弦进展。将该方法与传统的隐马尔可夫模型和基于DNN-HMM的方法进行了定量和定性评价。该模型分别比其它模型提高了23.8%和11.4%的性能。用户研究进一步证实，所提出的方法产生的和弦序列是听众的首选。
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英文标题：
《Chord Generation from Symbolic Melody Using BLSTM Networks》
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作者：
Hyungui Lim, Seungyeon Rhyu and Kyogu Lee
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最新提交年份：
2017
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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：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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：
Generating a chord progression from a monophonic melody is a challenging problem because a chord progression requires a series of layered notes played simultaneously. This paper presents a novel method of generating chord sequences from a symbolic melody using bidirectional long short-term memory (BLSTM) networks trained on a lead sheet database. To this end, a group of feature vectors composed of 12 semitones is extracted from the notes in each bar of monophonic melodies. In order to ensure that the data shares uniform key and duration characteristics, the key and the time signatures of the vectors are normalized. The BLSTM networks then learn from the data to incorporate the temporal dependencies to produce a chord progression. Both quantitative and qualitative evaluations are conducted by comparing the proposed method with the conventional HMM and DNN-HMM based approaches. Proposed model achieves 23.8% and 11.4% performance increase from the other models, respectively. User studies further confirm that the chord sequences generated by the proposed method are preferred by listeners.
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PDF链接：
https://arxiv.org/pdf/1712.01011
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