Style-Specific Turkish Pop Music Composition with CNN and LSTM Network

Tanberk, Senem; Tükel, Dilek

doi:10.1109/sami50585.2021.9378654

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…In contrast, compared with machine learning–based methods, deep learning–based methods for music sentiment classification are more suitable for data-rich music classification, as they eliminate the costs in audio feature extraction. The most prevalent method in this field today is convolutional neural networks (CNNS; Vargas et al , 2021; Sheykhivand et al , 2020; Tanberk et al , 2021; Liu et al , 2021; Song et al , 2020; Fan, 2022), which are suitable for spatial data processing. Moreover, CNNs consist of a convolutional layer for extracting local features and a pooling layer for dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

Music sentiment classification based on an optimized CNN-RF-QPSO model

Tian

Yin

Gan

2023

DTA

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PurposeMusic sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.Design/methodology/approachA CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.FindingsThe model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.Originality/valueThe dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

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Section: Introductionmentioning

confidence: 99%

Music sentiment classification based on an optimized CNN-RF-QPSO model

Tian

Yin

Gan

2023

DTA

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“…The use of CNN has been widely implemented in the field of image classification [12]. The previously discussed networks are purposefully constructed to detect and extract identifiable patterns and features from visual data [13]. Similar methods are used to train these networks for the purpose of recognizing patterns and features in musical sequences.…”

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“…CNN is often integrated with other deep learning techniques, such as LSTM, to generate complex and sophisticated musical compositions [13]. The LSTM network is a variant of the Recurrent Neural Network (RNN), which is able to effectively capture long-term temporal dependencies in time-series data, including musical sequences.…”

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confidence: 99%

“…The combination of CNN and LSTM networks produces both short-term and long-term musical patterns, resulting in more authentic and rationally structured music [13] [18]. CNN-LSTM has several advantages, including the ability to perform temporal analysis while extracting abstract features [19], and it outperforms standard machine learning algorithms in terms of stability, accuracy, and prediction [20][21] [22].…”

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confidence: 99%

“…However, previous research on music generation using the CNN-LSTM combination is limited to the generation of new melodies in Turkish pop music with a certain style [13] and modern music [18] from MIDI files. In this study, the same approach is used to generate music notation for several instruments based on variations in the structure of Javanese gamelan songs using notation-based music datasets.…”

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Deep Learning for Multi-Structured Javanese Gamelan Note Generator

Kurniawati,

Yuniarno,

Suprapto

2023

Knowl. Eng. Data Sci.

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Javanese gamelan, a traditional Indonesian musical style, has several song structures called gendhing. Gendhing (songs) are written in conventional notation and require gamelan musicians to recognize patterns in the structure of each song. Usually, previous research on gendhing focuses on artistic and ethnomusicological perspectives, but this study is to explore the correlation between gendhing as traditional music in Indonesia and deep learning technology that replaces the task of gamelan composers. This research proposes CNN-LSTM to generate notation of ricikan struktural instruments as an accompaniment to Javanese gamelan music compositions based on balungan notation, rhythm, song structure, and gatra information. This proposed method (CNN-LSTM) is compared with LSTM and CNN. The musical data in this study is represented using numerical notation for the main melody in balungan notation. The experimental results showed that the CNN-LSTM model showed better performance compared to the LSTM and CNN models, with accuracy values of 91.9%, 91.5%, and 91.2% for CNN-LSTM, LSTM, and CNN, respectively. And the value of note distance for the Sampak song structure is 4 for the CNN-LSTM model, 8 for the LSTM model, and 12 for the CNN model. The smaller the note distance, the closer it is to the original notation provided by the gamelan composer. This study provides relevance for novice gamelan musicians who are interested in learning karawitan, especially in understanding ricikan struktural music notation and gamelan art in composing musical compositions of a song.

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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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Style-Specific Turkish Pop Music Composition with CNN and LSTM Network

Cited by 6 publications

References 11 publications

Music sentiment classification based on an optimized CNN-RF-QPSO model

Music sentiment classification based on an optimized CNN-RF-QPSO model

Deep Learning for Multi-Structured Javanese Gamelan Note Generator

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

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