随着生成模型的普及，与之同步兴起的一个领域是音乐生成模型。本研究旨在比较简单马尔可夫链模型和循环神经网络（RNN）模型在爵士乐即兴演奏中的表现，这两种模型都是序列生成任务中的流行模型。虽然音乐，尤其是爵士乐，在判断一首作品是“好”还是“坏”时具有主观性，但我们旨在使用律动模式相似性和音高级别直方图熵等指标来量化我们的结果。我们使用专业爵士乐手演奏的爵士布鲁斯合唱转录本来训练这两个模型，并输入了音乐爵士乐种子，以帮助模型在开始生成时获得一些上下文。我们的结果表明，RNN在我们的两项指标上均优于马尔可夫模型，这表明生成的音乐具有更好的节奏一致性和调性稳定性。通过使用 music21 库，我们将爵士乐数据集标记化为模型可以解释和训练的音高和持续时间。我们的发现为不断发展的 AI 生成音乐领域做出了贡献，突出了使用指标评估生成质量的重要性。未来的工作包括将 MIDI 文件数据集扩大到更大规模，进行人类调查以进行主观评估，并纳入额外的指标以应对音乐评估中主观性的挑战。我们的研究为将循环神经网络用于生成音乐等基于序列的任务提供了有价值的见解。
英文摘要：
As generative models have risen in popularity, a domain that has risen alongside is generative models for music. Our study aims to compare the performance of a simple Markov chain model and a recurrent neural network (RNN) model, two popular models for sequence generating tasks, in jazz music improvisation. While music, especially jazz, remains subjective in telling whether a composition is "good" or "bad", we aim to quantify our results using metrics of groove pattern similarity and pitch class histogram entropy. We trained both models using transcriptions of jazz blues choruses from professional jazz players, and also fed musical jazz seeds to help give our model some context in beginning the generation. Our results show that the RNN outperforms the Markov model on both of our metrics, indicating better rhythmic consistency and tonal stability in the generated music. Through the use of music21 library, we tokenized our jazz dataset into pitches and durations that our model could interpret and train on. Our findings contribute to the growing field of AI-generated music, highlighting the important use of metrics to assess generation quality. Future work includes expanding the dataset of MIDI files to a larger scale, conducting human surveys for subjective evaluations, and incorporating additional metrics to address the challenge of subjectivity in music evaluation. Our study provides valuable insight into the use of recurrent neural networks for sequential based tasks like generating music.
Institute of Information Science, Academia Sinica（中央研究院信息科学研究所）；Taiwan AI Labs（台湾人工智能实验室）；Research Center for IT Innovation, Academia Sinica（中央研究院信息科技创新研究中心）
In this paper, we tackle the problem of transfer learning for Jazz automatic generation. Jazz is one of representative types of music, but the lack of Jazz data in the MIDI format hinders the construction of a generative model for Jazz. Transfer learning is an approach aiming to solve the problem of data insufficiency, so as to transfer the common feature from one domain to another. In view of its success in other machine learning problems, we investigate whether, and how much, it can help improve automatic music generation for under-resourced musical genres. Specifically, we use a recurrent v…
Ss Cyril and Methodius University in Skopje（斯科普里圣西里尔与美多德大学）；Faculty of Electrical Engineering and Information Technologies（电气工程与信息技术学院）
Music transcription is the process of transcribing music audio into music notation. It is a field in which the machines still cannot beat human performance. The main motivation for automatic music transcription is to make it possible for anyone playing a musical instrument, to be able to generate the music notes for a piece of music quickly and accurately. It does not matter if the person is a beginner and simply struggles to find the music score by searching, or an expert who heard a live jazz improvisation and would like to reproduce it without losing time doing manual transcription. We prop…
Indian Institute of Information Technology, Sri City（斯里城印度信息技术学院）
Realistic music generation has always remained as a challenging problem as it may lack structure or rationality. In this work, we propose a deep learning based music generation method in order to produce old style music particularly JAZZ with rehashed melodic structures utilizing a Bi-directional Long Short Term Memory (Bi-LSTM) Neural Network with Attention. Owing to the success in modelling long-term temporal dependencies in sequential data and its success in case of videos, Bi-LSTMs with attention serve as the natural choice and early utilization in music generation. We validate in our expe…
We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of handling polyphonic music with multiple instrument tracks, as well as modeling the dynamics of music by incorporating note durations and velocities. We show that MIDI-VAE can perform style transfer on symbolic music by automatically changing pitches, dynamics and instruments of a music piece from, e.g., a Classical to a Jazz style. We evaluate the efficacy of the style transfer by training separate style validation classifiers. Our model can also interpolate between short pieces of music, produce…
National Taiwan University（台湾大学）；Taiwan AI Labs（台湾人工智能实验室）；Academia Sinica（中央研究院）
This paper presents the Jazz Transformer, a generative model that utilizes a neural sequence model called the Transformer-XL for modeling lead sheets of Jazz music. Moreover, the model endeavors to incorporate structural events present in the Weimar Jazz Database (WJazzD) for inducing structures in the generated music. While we are able to reduce the training loss to a low value, our listening test suggests however a clear gap between the average ratings of the generated and real compositions. We therefore go one step further and conduct a series of computational analysis of the generated comp…
We present a new approach to harmonic analysis that is trained to segment music into a sequence of chord spans tagged with chord labels. Formulated as a semi-Markov Conditional Random Field (semi-CRF), this joint segmentation and labeling approach enables the use of a rich set of segment-level features, such as segment purity and chord coverage, that capture the extent to which the events in an entire segment of music are compatible with a candidate chord label. The new chord recognition model is evaluated extensively on three corpora of classical music and a newly created corpus of rock music…
Deep learning for music generation. Four approaches and their comparative evaluation
Faculty of Automatics and Computer Science, University POLITEHNICA of Bucharest（布加勒斯特理工大学自动控制与计算机科学学院）；Romanian Academy Research Institute for Artificial Intelligence（罗马尼亚科学院人工智能研究所）；Academy of Romanian Scientists（罗马尼亚科学院）
Music Genre Classification: A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches
Computer Science and Engineering（计算机科学与工程）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tribhuvan University, IOE（特里布文大学理工学院）；Arlington, USA（美国阿灵顿）；kathmandu（加德满都）；Dharan（达尔汉）
Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of inpu…
Pitch and meter are two fundamental music features for symbolic music generation tasks, where researchers usually choose different encoding methods depending on specific goals. However, the advantages and drawbacks of different encoding methods have not been frequently discussed. This paper presents a integrated analysis of the influence of two low-level feature, pitch and meter, on the performance of a token-based sequential music generation model. First, the commonly used MIDI number encoding and a less used class-octave encoding are compared. Second, an dense intra-bar metric grid is impose…
SONY CSL（索尼计算机科学实验室）；Sorbonne Universités（索邦大学）；UPMC Univ Paris 06（巴黎第六大学）
In the context of contemporary monophonic music, expression can be seen as the difference between a musical performance and its symbolic representation, i.e. a musical score. In this paper, we show how Maximum Entropy (MaxEnt) models can be used to generate musical expression in order to mimic a human performance. As a training corpus, we had a professional pianist play about 150 melodies of jazz, pop, and latin jazz. The results show a good predictive power, validating the choice of our model. Additionally, we set up a listening test whose results reveal that on average, people significantly …
Decoding Musical Evolution Through Network Science
Department of Computer Science, Sapienza University of Rome（罗马大学计算机科学系）；Department of Computer, Control and Management Engineering, Sapienza University of Rome（罗马大学计算机、控制与管理工程系）；University of Padova, Department of Mathematics（帕多瓦大学数学系）
Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This paper revisits artist classification with this new framework and empirically explores the impacts of incorporating temporal structure in the feature representation. To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied …
Polish-Japanese Institute of Information Technology（波日信息技术学院）；Interdisciplinary Centre for Mathematical and Computational Modelling (ICM), University of Warsaw（华沙大学跨学科数学与计算建模中心（ICM））
In this paper, we first apply random ferns for classification of real music recordings of a jazz band. No initial segmentation of audio data is assumed, i.e., no onset, offset, nor pitch data are needed. The notion of random ferns is described in the paper, to familiarize the reader with this classification algorithm, which was introduced quite recently and applied so far in image recognition tasks. The performance of random ferns is compared with random forests for the same data. The results of experiments are presented in the paper, and conclusions are drawn.
Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times of notes and to recognize the corresponding instrument sources. The aim of this study is to explore the usefulness of multiscale scattering operators for these two tasks on plucked string instrument and piano music. After resuming the theoretical background and illustrating the …
ENSTA Bretagne（布列塔尼国立高等先进技术学校）；Université Européenne de Bretagne（布列塔尼欧洲大学）；Université Paris Descartes（巴黎笛卡尔大学）
Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data, and have been widely used in two main tasks of Automatic Music Transcription (AMT): note segmentation, i.e. identifying the played notes after a multi-pitch estimation, and sequential post-processing, i.e. correcting note segmentation using training data. In this paper, we employ the multi-pitch estimation method called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems by integrating different HMM-based modules in this framework. For note segmentation, we use two different twostate on/o? HMMs, in…
Many methods for automatic music transcription involves a multi-pitch estimation method that estimates an activity score for each pitch. A second processing step, called note segmentation, has to be performed for each pitch in order to identify the time intervals when the notes are played. In this study, a pitch-wise two-state on/off firstorder Hidden Markov Model (HMM) is developed for note segmentation. A complete parametrization of the HMM sigmoid function is proposed, based on its original regression formulation, including a parameter alpha of slope smoothing and beta? of thresholding cont…
Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Universidade Federal do Estado do Rio de Janeiro（里约热内卢州联邦大学）；Pontifícia Universidade Católica do Rio de Janeiro（里约热内卢天主教大学）；Sony Computer Science Laboratories（索尼计算机科学实验室）；Spotify Creator Technology Research Lab（Spotify创作者技术研究实验室）
This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and b…
The relationship between sensory consonance and Western harmony is an important topic in music theory and psychology. We introduce new methods for analysing this relationship, and apply them to large corpora representing three prominent genres of Western music: classical, popular, and jazz music. These methods centre on a generative sequence model with an exponential-family energy-based form that predicts chord sequences from continuous features. We use this model to investigate one aspect of instantaneous consonance (harmonicity) and two aspects of sequential consonance (spectral distance and…
Simon Fraser University（西蒙菲莎大学）；Academia Sinica（中央研究院）；Dartmouth College（达特茅斯学院）
Whether literally or suggestively, the concept of soundscape is alluded in both modern and ancient music. In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models. We addressed this question through a comparative study. Specifically, corpora of Western classical music excerpts (WCMED) and Chinese classical music excerpts (CCMED) were curated and annotated with emotional valence and arousal through a crowdsourcing experiment. We used a sound event detection (SED) and soundscape emotion recognition (SER) models with transfer lear…
Polyphonic music files were analyzed using the set of symbols that produced the Minimal Entropy Description which we call the Fundamental Scale. This allowed us to create a novel space to represent music pieces by developing: a) a method to adjust a description from its original scale of observation to a general scale, b) the concept of higher order entropy as the entropy associated to the deviations of a frequency ranked symbol profile from a perfect Zipf profile. We called this diversity index the "2nd Order Entropy". Applying these methods to a variety of musical pieces showed how the space…
Universidade Federal do Estado do Rio de Janeiro（里约热内卢联邦大学）；Sorbonne Université（索邦大学）
This paper presents a comparative analysis on two artificial neural networks (with different architectures) for the task of tempo estimation. For this purpose, it also proposes the modeling, training and evaluation of a B-RNN (Bidirectional Recurrent Neural Network) model capable of estimating tempo in bpm (beats per minutes) of musical pieces, without using external auxiliary modules. An extensive database (12,550 pieces in total) was curated to conduct a quantitative and qualitative analysis over the experiment. Percussion-only tracks were also included in the dataset. The performance of the…
This paper studies the prediction of chord progressions for jazz music by relying on machine learning models. The motivation of our study comes from the recent success of neural networks for performing automatic music composition. Although high accuracies are obtained in single-step prediction scenarios, most models fail to generate accurate multi-step chord predictions. In this paper, we postulate that this comes from the multi-scale structure of musical information and propose new architectures based on an iterative temporal aggregation of input labels. Specifically, the input and ground tru…
The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of structural boundaries of the music pieces. This structural boundary analysis has recently been studied with unsupervised methods and \textit{end-to-end} techniques such as Convolutional Neural Networks (CNN) using Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs and trained with human annotations. Several studies have been p…
