Automatic Music Generation (AMG) has become an interesting research topic for many scientists in artificial intelligence, who are also interested in the music industry. One of the main challenges in AMG is that there is no clear objective evaluation criterion that can measure the music grammar, structural rules, and audience satisfaction. Also, original music contains different elements that should work together, such as melody, harmony, and rhythm; but in the most of previous works, AMG works only for one element (e.g., melody). Therefore, in this paper, we propose a Multi-Objective Genetic Algorithm (MO-GA) to generate polyphonic music pieces, considering grammar and listener satisfaction. In this method, we use three objective functions. The first objective function is the accuracy of the generated music piece, based on music theory; and the other two objective functions are modeled scores provided by music experts and ordinary listeners. The scoring of experts and listeners separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks. The proposed music generation system tries to maximize mentioned objective functions to generate a new piece of music, including melody and harmony. The results show that the proposed method can generate pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar.
Tel-Aviv University（特拉维夫大学）；Facebook AI Research（Facebook人工智能研究院）
We present a method for the generation of Midi files of piano music. The method models the right and left hands using two networks, where the left hand is conditioned on the right hand. This way, the melody is generated before the harmony. The Midi is represented in a way that is invariant to the musical scale, and the melody is represented, for the purpose of conditioning the harmony, by the content of each bar, viewed as a chord. Finally, notes are added randomly, based on this chord representation, in order to enrich the generated audio. Our experiments show a significant improvement over t…
This paper presents an adaptation of the harmony search algorithm to solve the storage allocation problem for inbound and outbound containers. This problem is studied considering multiple container type (regular, open side, open top, tank, empty and refrigerated) which lets the situation more complicated, as various storage constraints appeared. The objective is to find an optimal container arrangement which respects their departure dates, and minimize the re-handle operations of containers. The performance of the proposed approach is verified comparing to the results generated by genetic algo…
Southern University of Science and Technology（南方科技大学）
Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation consider only one type of content at a time. In this paper, we propose and formulate online level generation from music, in a way of matching a level feature to a music feature in real-time, while adapting to players' play speed. A generic framework named online player-adaptive procedural content generation via reinforcement learning, OPARL for short, is built upon the experience-driven reinforcement learning and co…
Media Engineering Faculty, Iran Broadcasting University（伊朗广播大学媒体工程学院）
Music Generation (MG) is an interesting research topic that links the art of music and Artificial Intelligence (AI). The goal is to train an artificial composer to generate infinite, fresh, and pleasurable musical pieces. Music has different parts such as melody, harmony, and rhythm. In this paper, we propose a Generative Genetic Algorithm (GGA) to produce a melody automatically. The main GGA uses a Long Short-Term Memory (LSTM) recurrent neural network as the objective function, which should be trained by a spectrum of bad-to-good melodies. These melodies have to be provided by another GGA wi…
This paper explores the idea of utilising Long Short-Term Memory neural networks (LSTMNN) for the generation of musical sequences in ABC notation. The proposed approach takes ABC notations from the Nottingham dataset and encodes it to be fed as input for the neural networks. The primary objective is to input the neural networks with an arbitrary note, let the network process and augment a sequence based on the note until a good piece of music is produced. Multiple calibrations have been done to amend the parameters of the network for optimal generation. The output is assessed on the basis of r…
SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton
Department of AI Music and Music Information Technology, Central Conservatory of Music（中央音乐学院人工智能音乐与音乐信息科技系）；Frontier Institute of Science and Technology, and Interdisciplinary Research Center of Frontier Science and Technology, Xi’an Jiaotong University（西安交通大学前沿科学与技术研究院及交叉科学与技术 interdisciplinary research center）；University of Science and Technology of China（中国科学技术大学）；Shenzhen University（深圳大学）
Musical counterpoint, a musical technique in which two or more independent melodies are played simultaneously with the goal of creating harmony, has been around since the baroque era. However, to our knowledge computational generation of aesthetically pleasing linear counterpoint based on subjective fitness assessment has not been explored by the evolutionary computation community (although generation using objective fitness has been attempted in quite a few cases). The independence of contrapuntal melodies and the subjective nature of musical aesthetics provide an excellent platform for the a…
Deep generative models for symbolic music are typically designed to model temporal dependencies in music so as to predict the next musical event given previous events. In many cases, such models are expected to learn abstract concepts such as harmony, meter, and rhythm from raw musical data without any additional information. In this study, we investigate the effects of explicitly conditioning deep generative models with musically relevant information. Specifically, we study the effects of four different conditioning inputs on the performance of a recurrent monophonic melody generation model. …
Peking University（北京大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Melody is one of the most important components in music. Unlike other components in music theory, such as harmony and counterpoint, computable features for melody is urgently in need. These features are highly demanded as data-driven methods dominating the fields such as musical information retrieval and automatic music composition. To boost the performance of deep-learning-related musical tasks, we propose a set of interpretable features on monophonic melody for computational purposes. These features are defined not only in mathematical form, but also with some considerations on composers 'in…
School of Business, Guangxi University（广西大学商学院）；School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）；School of Business Administration, Southwestern University of Finance and Economics（西南财经大学工商管理学院）
Multi-view MidiVAE: Fusing Track- and Bar-view Representations for Long Multi-track Symbolic Music Generation
Tsinghua University（清华大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）；Skywork AI PTE. LTD.（昆仑万维人工智能（Skywork AI））；The Chinese University of Hong Kong（香港中文大学）
ComposerX: Multi-Agent Symbolic Music Composition with LLMs
University of Rochester（罗切斯特大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Hong Kong University of Science and Technology（香港科技大学）；University of Surrey（萨里大学）；The University of Manchester（曼彻斯特大学）；Queen Mary University of London（伦敦玛丽女王大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
LeVo: High-Quality Song Generation with Multi-Preference Alignment
Shenzhen International Graduate School, Tsinghua University, Shenzhen（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯AI实验室）；Wuhan University（武汉大学）；The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））；X-LANCE Lab, Shanghai Jiao Tong University, Shanghai（上海交通大学X-LANCE实验室）；School of Intelligence Science and Technology, Nanjing University, Suzhou, China（南京大学智能科学与技术学院）
Pay (Cross) Attention to the Melody: Curriculum Masking for Single-Encoder Melodic Harmonization
Department of Music Technology（音乐技术系）；Acoustics Hellenic Mediterranean University, Greece Archimedes, Athena RC, Greece（声学 希腊地中海大学 阿里卡斯萨斯）；Institute of Language（语言研究所）；School of Music Studies Aristotle University of Thessaloniki, Greece（音乐研究学院 阿里斯托芬大学）
MuDiT & MuSiT: Alignment with Colloquial Expression in Description-to-Song Generation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Caichong Artificial Intelligence Research, DuiNiuTanQin Co., Ltd.（追牛探琴有限公司彩创人工智能研究院）
