Music Generation and Song Popularity Prediction using Artificial Intelligence - An Overview

Jabade, Vaishali; Deshpande, Vedang M.; Aditya, K

doi:10.5120/ijca2019918762

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…TMS320VC5502 chip includes 6 channels of memory direct access controller; 3 multi-channel buffered serial ports; Programmable analogy PLL clock generator; General purpose I / O pin (GPIO) and special output pin (XF); 8-bit / 16-bit parallel host interface; 4 timers: two 64-bit general purpose timers, one 64-bit programmable watchdog timer, one 64-bit DSP/BIOS counter; 12C interface; Universal asynchronous transceiver (UART); TMS320VC5502 has 16MB storage space. When a certain space is used as a data space [18], the minimum addressing unit is 16-bit; When a certain space is used as program space, the minimum addressing unit is 8-bit.…”

Section: Dsp Control Chipmentioning

confidence: 99%

Design of music training assistant system based on artificial intelligence

Zhihan¹,

Liang²,

Jin³

2018

ICST Transactions on Scalable Information Systems

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In order to improve the input accuracy and response speed of music training, this paper designs an intelligent assistant system. The architecture is divided into infrastructure layer, data layer, application layer and presentation layer. In the hardware design, the combination of ARM and digital signal processor (DSP) is used to realize the interaction between data analysis and human and interface. In the software design, cepstrum algorithm is used to extract cepstrum features of music signals, linear smoothing algorithm is used to filter, dynamic time warping method is used to match patterns, and radial basis function algorithm is used to output the results. Thus, the overall design of the music-assisted training system is completed. Experimental results show that the signal-to-noise ratio of music signal transmission is more than 14dB, the accuracy is higher than 99.5%, and the response speed of serving 240 users is only 1s. The system has strong operability and good performance of music assistant training.

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Section: Dsp Control Chipmentioning

confidence: 99%

Design of music training assistant system based on artificial intelligence

Zhihan¹,

Liang²,

Jin³

2018

ICST Transactions on Scalable Information Systems

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“…However, in today's digital era, music is widely accessible through online platforms like Spotify, Joox, iTunes, and others, with AI algorithms playing a significant role in recommendations and search results. Additionally, Kang, J. H. & Park (2020) harnessed AI as a predictive tool for music video popularity, similar to Jabade et al (2019), where AI was employed to gauge song popularity. These studies underscore the pivotal role of AI in data collection and analysis.…”

Section: Introductionmentioning

confidence: 99%

The rise of artificial intelligence as the future of Korean culture diplomacy

Amaliyah,

Jatmika,

Surwandono

2023

Indonesian Journal of Communications Studies

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The COVID-19 pandemic ushered in a new era of technology, pushing everything online and necessitating human adaptation to computers. Industry 4.0 proved invaluable during this pandemic, with Artificial Intelligence (AI) emerging as a key player, particularly in the beloved South Korean music industry, which enjoys international acclaim. The pandemic played a significant role in the increased use of AI within the K-POP industry, with the emergence of the Aespa Girl Group being a pivotal moment in shaping a futuristic concept. This research aims to investigate the recent surge in AI usage as a futuristic K-Pop concept through a qualitative literature study method. This paper contends that AI has the potential to profoundly impact Korean cultural diplomacy, serving as a game-changing element in the realm of digital diplomacy. While AI has traditionally been applied in high-context scenarios, such as search engines or high-tech automotive control systems, its application in cultural contexts could propel South Korea's cultural diplomacy to unprecedented heights, rendering the concept difficult for other nations to replicate.

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“…Since the 21st century, music robot technology has been widely developed, including different technologies such as the principle of music robot pronunciation, expressive performance, bionic structure, and intelligence. As one of the symbols of “AI,” the problem of music generation by music robots has become a research hotspot [ 5 ]. The most important thing in the field of music style recognition and generation is the extraction of relevant features and the selection of classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…It mainly contains five music genres: classical, country, pop, jazz, and rock. In addition, some scholars also perform music recognition by extracting the rhythm and pitch from the music features to form a twodimensional feature vector and finally achieve the effect of music recognition [5,[8][9][10][11]. Hizlisoys et al proposed a method for music emotion recognition based on a deep neural network (DNN) architecture with convolutional LSTM.…”

Section: Introductionmentioning

confidence: 99%

The Generation of Piano Music Using Deep Learning Aided by Robotic Technology

Jian¹,

Zhang³

et al. 2022

Computational Intelligence and Neuroscience

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In order to improve the accuracy and precision of music generation assisted by robotics, this study analyzes the application of deep learning in piano music generation. Firstly, based on the basic concepts of robotics and deep learning, the advantages of long short-term memory (LSTM) networks are introduced and applied to the piano music generation. Meanwhile, based on LSTM, dropout coefficients are used for optimization. Secondly, various parameters of the algorithm are determined, including the effects of the number of iterations and neurons in the hidden layer on the effect of piano music generation. Finally, the generated music sequence spectrograms are analyzed to illustrate the accuracy and rationality of the algorithm. The spectrograms are compared with the music sequence spectrograms generated by the traditional restricted Boltzmann machine (RBM) music generation algorithm. The results show that (1) when the dropout coefficient value is 0.7, the function converges faster, and the experimental results are better; (2) when the number of iterations is 6000, the error between the generated music sequence and the original music is the smallest; (3) the number of hidden layers of the network is set to 4. When the number of neurons in each hidden layer is set to 1024, the training result of the network is optimal; (4) compared with the traditional RBM piano music generation algorithm, the LSTM-based algorithm and the sampling frequency distribution tend to be consistent with the original sample. The results show that the network has good performance in music generation and can provide a certain reference for automatic music generation.

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Music Generation and Song Popularity Prediction using Artificial Intelligence - An Overview

Cited by 3 publications

References 13 publications

Design of music training assistant system based on artificial intelligence

Design of music training assistant system based on artificial intelligence

The rise of artificial intelligence as the future of Korean culture diplomacy

The Generation of Piano Music Using Deep Learning Aided by Robotic Technology

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