Automatic Music Generator Using Recurrent Neural Network

Gunawan, Alexander Agung Santoso; Iman, Ananda Phan; Suhartono, Derwin

doi:10.2991/ijcis.d.200519.001

Cited by 25 publications

(13 citation statements)

References 7 publications

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“…In recent years, the development of deep learning techniques has provided methods for extracting high-dimensional features from raw data and has opened up new possibilities in the fields of motion generation and automatic music choreography [17][18][19][20][21]. Recurrent neural network (RNN) has been considered as an effective means to solve sequential tasks and has been used in natural language processing (NLP) [22], speech recognition [23], music composition [24], and other fields. However, traditional RNNs suffer from the gradient disappearance problem, which can seriously affect the effect when the sequence length increases.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

Cang

Huang

2021

Computational Intelligence and Neuroscience

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Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

Cang

Huang

2021

Computational Intelligence and Neuroscience

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“…Generally speaking, the higher the value of intensity, the more majestic the music, and the lower the value of intensity, the more subdued the music. For beginners, the most common mistake is that they are not able to control the intensity of their playing, and they tend to ignore the intensity markings on the score, thinking that the only important components of music are the notes, which often results in joyless playing [21][22][23][24]. As we usually say, "lack of musicality" refers to the lack of richness and variation in the intensity of a player's playing.…”

Section: Analysis Of Musical Performance Stylementioning

confidence: 99%

[Retracted] Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles

Wang

Geng

2021

Complexity

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This paper presents a theoretical framework of the circular shift network coding system through the study of nonmultiple clustered interval music performance style conversion and the analysis of music conversion by using circular shift topology, and a series of basic research results of circular shift network coding is obtained under this framework. It reveals the essential connection between scalar network coding based on finite domain and cyclic shift network coding, designs a solution construction algorithm for cyclic shift network coding under multicast network, and portrays the multicast capacity of cyclic shift network coding. It overcomes the problem that the piano roll-curtain representation cannot distinguish between a single long note and multiple consecutive notes of the same pitch, describes musical information more comprehensively, extracts musical implicit style from the note matrix based on autoencoder, and better eliminates the potential influence of musical content on musical performance style. A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music.

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“…Music data can be trained after processing ( Hughes et al, 2018 ). In this model, track separation, music feature extraction, and data vectorization are mainly performed on the data ( Gunawan et al, 2020 ). Figure 2 presents the architectural design of the model.…”

Section: Methodsmentioning

confidence: 99%

The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

Tang

Zhang²,

Zhang³

2022

Front. Psychol.

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The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method.

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Automatic Music Generator Using Recurrent Neural Network

Cited by 25 publications

References 7 publications

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

[Retracted] Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles

The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching

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