Generation of music pieces using machine learning: long short-term memory neural networks approach

Hewahi, Nabil M.; AlSaigal, Salman; AlJanahi, Sulaiman

doi:10.1080/25765299.2019.1649972

Cited by 22 publications

(7 citation statements)

References 3 publications

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“…The choice of model depends on the specific music generation goals, the available data and the desired characteristics of the generated music. In general, the construction principle of these models is based on learning-recall, they have a huge "memory" that works by association (Hewahi, AlSaigal and AlJanahi, 2019). Unlike previous heuristic models based on Markov chains, neural networks manage to capture longer dependencies in a song and process them.…”

Section: The Use Of Generative Models Based On Neural Networkmentioning

confidence: 99%

The Impact of Generative AI in Music Composition / Impactul inteligenței artificiale generative în compoziția muzicală

Mihăescu,

Mihăescu

2023

TICDM / ICTMF

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This paper briefly describes the concept of generative artificial intelligence in music composition, highlighting the innovations and challenges brought by this technology.After an overview of the types of models used in training generative algorithms and specialised software for music composition, the case study analyses two of these applications. The article concludes with a reflection on the challenges brought by this technology and how these systems support musicians in the creative, compositional act.

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Section: The Use Of Generative Models Based On Neural Networkmentioning

confidence: 99%

The Impact of Generative AI in Music Composition / Impactul inteligenței artificiale generative în compoziția muzicală

Mihăescu,

Mihăescu

2023

TICDM / ICTMF

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“…e papers use various machine learning and deep approaches which are used in developing the models in both training and test parts of our models [6][7][8]. Hewahi et al discuss a method for pieces of music generation using machine learning where the long-short-term memory neural networks approach is used [9]. is paper uses Bach's musical style for a generation using MIDI files by converting these into song files.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format

Yadav¹,

Khan²,

Singh

et al. 2022

Computational Intelligence and Neuroscience

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In today’s real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.

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“…With the development of Artificial Neural Network (ANN) and several learning algorithms introduced using ANN, the music generating models created by such supervised learning algorithms plays a significant role in music synthesis [9] [10] [11] [12] [13] [14]. The usage of LSTM in music generation of Bach's music style was implemented using a trained neural network by considering arbitrary datasets [15]. Aiva IA, an AI-based automatic music synthesizer, has its own copyrighted audio tracks [16].…”

Section: Related Work In Music Generationmentioning

confidence: 99%

Investigation on Automatic Music Generation Using Gan and Lstm Networks

Tony

Sasikumar

2022

Preprint

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In this article, the authors propose a few methodologies for composing music using deep learning algorithms and Long-short term memory (LSTM) neural network and Generative Adversarial Networks (GANs). The LSTM model is created by training with a set of input files from a music library. The trained model then synthesizes music when an arbitrary note is provided. The GAN and other variants are trained using a set of midi file accumulated from the piano dataset. The pre-trained GAN model is then used to generate music similar to piano roll. The quality of the music is calculated by comparing the harmony and few other parameters of the synthesized music with the trained files. The music library is made with a set of midi files and based on the chosen library, a unique model shall be created. For the model creation, the library files are converted into a suitable format and encoded in order to make it compatible with the LSTM network. Though the outcome of this experiment is a continuous music, the harmony and notes can still be improved to solve the discontinuity problem. The outcome of this experiment is evaluated using conventional evaluators and also the aesthetics by human observer.

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Generation of music pieces using machine learning: long short-term memory neural networks approach

Cited by 22 publications

References 3 publications

The Impact of Generative AI in Music Composition / Impactul inteligenței artificiale generative în compoziția muzicală

The Impact of Generative AI in Music Composition / Impactul inteligenței artificiale generative în compoziția muzicală

A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format

Investigation on Automatic Music Generation Using Gan and Lstm Networks

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