Genetic algorithms for music variation on genom platform

Arutyunov, Vsevolod; Averkin, Alexey

doi:10.1016/j.procs.2017.11.245

Cited by 9 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Pearson's Correlation of Coefficient is exploited in this work. The work presented in [21] is a method to generate song list for listening; the songs may be downloaded according to age factor of the online users. It is a web-based application that recommends different songs depending on the listeners' choice that too based on their age group.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Discover Similar Musical Patterns

Chakrabarty

Islam²,

Pricop

et al. 2022

IEEE Access

View full text Add to dashboard Cite

An algorithm has been developed to find the similarity between given songs. The song pattern similarity has been determined by knowing the note structures and the fundamental frequencies of each note of the two songs, under consideration. The statistical concept namely Correlation of Coefficient is used in this work. Correlation of Coefficient is determined by applying 16 Note-Measure Method. If Correlation of Coefficient is near to 1, it indicates that the patterns of the two songs under consideration are similar. Otherwise, there exists a certain percentage of similarity only. This basic principle is used in a set of Indian Classical Music (ICM) based songs. The proposed algorithm can determine the similarity between songs, so that alternative songs in place of some well-known songs can be identified, in terms of the embedded raga patterns. A digital music library has been constructed as a part of this work. The library consists of different songs, their raga name, and their corresponding healing capabilities in terms of music therapy. The proposed work may find application in the area of music therapy. Music therapy is an area of research which is explored significantly in recent time. This work can also be exploited for developing intelligent multimedia tool that is applicable in healthcare domain. A multimedia based mobile app has been developed encapsulating the above mentioned idea that can recommend alternative or similar songs to the existing ICM based songs. This mobile app based music recommendation system may be used for different purposes including entertainment and healthcare. As a result of the applications of the proposed algorithm, similar songs in terms of raga patterns can be discovered from within the pool of a set of songs. A music recommendation system built on this algorithm can retrieve an alternative song from within the pool of songs as a replacement to a wellknown song, which otherwise may be used for a particular music therapy. Results are reported and analyzed thoroughly. Future scope of the work is outlined.INDEX TERMS Fundamental frequency measure (FFM), correlation of coefficient, computational musicology, music recommendation system (MIR), music therapy, electronic healthcare.

show abstract

Section: Related Workmentioning

confidence: 99%

An Approach to Discover Similar Musical Patterns

Chakrabarty

Islam²,

Pricop

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The next occurred in 1960, when R. Kh. Zaripov explored the use of a computer for creating short monophonic melodies, with Zaripov later identifying the creation of melodies as being one of the most important aspects of the process [1].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Generative Adversarial Network, via Transfer Learning, for Traditional Scottish Music Generation

Marchetti

Wilson

Powell

et al. 2021

Artificial Intelligence in Music, Sound, Art and Design

View full text Add to dashboard Cite

The concept of a Binary Multi-track Sequential Generative Adversarial Network (BinaryMuseGAN) used for the generation of music has been applied and tested for various types of music. However, the concept is yet to be tested on more specific genres of music such as traditional Scottish music, for which extensive collections are not readily available. Hence exploring the capabilities of a Transfer Learning (TL) approach on these types of music is an interesting challenge for the methodology. The curated set of MIDI Scottish melodies was preprocessed in order to obtain the same number of tracks used in the BinaryMuseGAN model; converted into pianoroll format and then used as training set to fine tune a pretrained model, generated from the Lakh MIDI dataset. The results obtained have been compared with the results obtained by training the same GAN model from scratch on the sole Scottish music dataset. Results are presented in terms of variation and average performances achieved at different epochs for five performance metrics, three adopted from the Lakh dataset (qualified note rate, polyphonicity, tonal distance) and two custom defined to highlight Scottish music characteristics (dotted rhythm and pentatonic note). From these results, the TL method shows to be more effective, with lower number of epochs, to converge stably and closely to the original dataset reference metrics values.

show abstract

“…Some of the previous research in this discipline is based on the identification of the chords and the progressions themselves [2]. Others, center their efforts in using machine learning techniques to generate music chords progression, based on songs that are used to build the model and taking into account only their musical score [3][4][5]. Related work in this field of study combines musical features with the song lyrics [6] to classify the songs or use specific musical features to divide the songs into emotions [7].…”

Section: Introductionmentioning

confidence: 99%

Chord Progressions Selection Based on Song Audio Features

Rico

Dı́az

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A chord progression is an essential building block in music. In the field of music theory is usually assumed that these progressions influence the mood, emotion, genre or other critical aspects of the songs, and also in the perception that they will cause on the listener. Therefore, it is natural to think that musical and audio features of a track should be related to its chord progressions. Choosing carefully these progressions when it comes the time of creating a new song, is a fundamental aspect depending on the feelings we want to evoke on the listener. Also, two songs can be considered alike or classified into the same emotions or genres if they use the same chord progressions. Many music classification studies are presented nowadays, but none of them take into account chord progressions, probably due to the lack of this kind of data. In this paper, classification algorithms are used to illustrate the influence of the songs' features when it comes to pick up chord progressions to create a new song.

show abstract

Genetic algorithms for music variation on genom platform

Cited by 9 publications

References 5 publications

An Approach to Discover Similar Musical Patterns

An Approach to Discover Similar Musical Patterns

Convolutional Generative Adversarial Network, via Transfer Learning, for Traditional Scottish Music Generation

Chord Progressions Selection Based on Song Audio Features

Contact Info

Product

Resources

About