Automatic Music Generation by Deep Learning

2018 ◽  
pp. 284-291
Author(s):  
Juan Carlos García ◽  
Emilio Serrano
Keyword(s):  
Deep Learning ◽  
Music Generation ◽  
Automatic Music Generation

scholarly journals INCO-GAN: Variable-Length Music Generation Method Based on Inception Model-Based Conditional GAN

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 387
Author(s):  
Shuyu Li ◽  
Yunsick Sung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Generative Models ◽  
Variable Length ◽  
Sequential Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Music Generation ◽  
Automatic Music Generation ◽  
Frequency Vectors

Deep learning has made significant progress in the field of automatic music generation. At present, the research on music generation via deep learning can be divided into two categories: predictive models and generative models. However, both categories have the same problems that need to be resolved. First, the length of the music must be determined artificially prior to generation. Second, although the convolutional neural network (CNN) is unexpectedly superior to the recurrent neural network (RNN), CNN still has several disadvantages. This paper proposes a conditional generative adversarial network approach using an inception model (INCO-GAN), which enables the generation of complete variable-length music automatically. By adding a time distribution layer that considers sequential data, CNN considers the time relationship in a manner similar to RNN. In addition, the inception model obtains richer features, which improves the quality of the generated music. In experiments conducted, the music generated by the proposed method and that by human composers were compared. High cosine similarity of up to 0.987 was achieved between the frequency vectors, indicating that the music generated by the proposed method is very similar to that created by a human composer.

scholarly journals A Review of Automatic Music Generation Based on Performance RNN

2020 ◽  
Author(s):  
Jiyanbo Cao ◽  
Jinan Fiaidhi ◽  
Maolin Qi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Network Performance ◽  
State Of The Art ◽  
Learning Techniques ◽  
History Of ◽  
Music Generation ◽  
Automatic Music Generation

This paper has reviewed the deep learning techniques which used in music generation. The research was based on <i>Sageev Oore's</i> proposed LSTM based recurrent neural network (Performance RNN). We have study the history of automatic music generation, and now we are using a state of the art techniques to achieve this mission. We have conclude the process of making a MIDI file to a structure as input of Performance RNN and the network structure of it.

scholarly journals Review on Automatic Music Generation System with Deep learning

2021 ◽  
pp. 332-335
Author(s):  
Prof. Amita Suke ◽  
Prof. Khemutai Tighare ◽  
Yogeshwari Kamble
Keyword(s):  
Deep Learning ◽  
Generation System ◽  
Easy Task ◽  
Present Writing ◽  
Long Time ◽  
Music Generation ◽  
Human Skills ◽  
Automatic Music Generation ◽  
Music Lyrics

The music lyrics that we generally listen are human written and no machine involvement is present. Writing music has never been easy task, lot of challenges are involved to write because the music lyrics need to be meaningful and at the same time it needs to be in harmony and synchronised with the music being play over it. They are written by experienced artist who have been writing music lyrics form long time. This project tries to automate music lyrics generation using computerized program and deep learning which we produce lyrics and reduce the load on human skills and may generate new lyrics and a really faster rate than humans ever can. This project will generate the music with the assistance of human and AI

scholarly journals A Review of Automatic Music Generation Based on Performance RNN

2020 ◽  
Author(s):  
Jiyanbo Cao ◽  
Jinan Fiaidhi ◽  
Maolin Qi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Network Performance ◽  
State Of The Art ◽  
Learning Techniques ◽  
History Of ◽  
Music Generation ◽  
Automatic Music Generation

This paper has reviewed the deep learning techniques which used in music generation. The research was based on <i>Sageev Oore's</i> proposed LSTM based recurrent neural network (Performance RNN). We have study the history of automatic music generation, and now we are using a state of the art techniques to achieve this mission. We have conclude the process of making a MIDI file to a structure as input of Performance RNN and the network structure of it.

Automatic Music Generation

2019 ◽  
Vol 7 (3) ◽  
pp. 80-82
Author(s):  
Lawakesh Patel ◽  
Nidhi Singh ◽  
Rizwan Khan
Keyword(s):  
Music Generation ◽  
Automatic Music Generation

Composer4Everyone: Automatic Music Generation with Audio Motif

2019 ◽  
Author(s):  
Aozhi Liu ◽  
Jianzong Wang ◽  
Junqing Peng ◽  
Yiwen Wang ◽  
Yaqi Mei ◽  
...  
Keyword(s):  
Music Generation ◽  
Automatic Music Generation

Security and Ethical Concerns of Affective Algorithmic Music Composition in Smart Spaces

2020 ◽  
pp. 193-203
Author(s):  
Abigail Wiafe ◽  
Pasi Fränti
Keyword(s):  
Ethical Issues ◽  
Denial Of Service ◽  
Algorithmic Composition ◽  
Smart Spaces ◽  
Ethical Implications ◽  
Algorithmic Music ◽  
Music Generation ◽  
Automatic Music Generation ◽  
Algorithmic Music Composition ◽  
Emotionally Intelligent

Affective algorithmic composition systems are emotionally intelligent automatic music generation systems that explore the current emotions or mood of a listener and compose an affective music to alter the person's mood to a predetermined one. The fusion of affective algorithmic composition systems and smart spaces have been identified to be beneficial. For instance, studies have shown that they can be used for therapeutic purposes. Amidst these benefits, research on its related security and ethical issues is lacking. This chapter therefore seeks to provoke discussion on security and ethical implications of using affective algorithmic compositions systems in smart spaces. It presents issues such as impersonation, eavesdropping, data tempering, malicious codes, and denial-of-service attacks associated with affective algorithmic composition systems. It also discusses some ethical implications relating to intensions, harm, and possible conflicts that users of such systems may experience.

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