Music Generation Based on Convolution-LSTM

Huang, Yusi; Xiao-feng, Huang; Cai, Qiakai

doi:10.5539/cis.v11n3p50

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…In [172] a Convolution-LSTM for piano track generation is considered. The CNN layer is used for feature extraction, and the output is fed into the LSTM for music generation.…”

Section: Cnnsmentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments in Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

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“…In [172] a Convolution-LSTM for piano track generation is considered. The CNN layer is used for feature extraction, and the output is fed into the LSTM for music generation.…”

Section: Cnnsmentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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“…CNNs are a promising approach for creating melodies. In fact, many researchers have used CNNs to create melodies [9,10,11]. A CNN can hierarchically reduce the temporal information in a melody by stacking convolution layers.…”

Section: Introductionmentioning

confidence: 99%

Generating Melodies from Melodic Outlines Towards an Improvisation Support System for Non-musicians

Kitahara

2023

IIAI Letters on Informatics and Interdisciplinary Research

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One promising approach for supporting non-musicians' music improvisation is to enable them to input the coarse shape of melodies they want to play. Therefore, drawing a melodic outline is adopted as an input method of existing improvisation support systems. However, generating melodies from melodic outlines with deep neural networks has not been fully exploited. In this paper, we report an attempt to generate melodies from melodic outlines using a convolutional neural network. Given a melodic outline, it is reduced with two convolution layers and then coverted to a sequence of notes with two deconvolution layers. Objective and subjective evaluations imply that our model with sufficient filter width and channels could generate melodies of moderate, close quality to human melodies.

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“…First, when generating music, the model cannot judge when to end the generation by itself. In other words, the model only can generate fixed-length music [15,16]. This means that the model cannot determine whether the currently generated segment belongs to the beginning, middle or end of the music.…”

Section: Introductionmentioning

confidence: 99%

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

Sung

2021

Mathematics

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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.

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Music Generation Based on Convolution-LSTM

Cited by 12 publications

References 3 publications

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Generating Melodies from Melodic Outlines Towards an Improvisation Support System for Non-musicians

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

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