Generative Music with Partitioned Quantum Cellular Automata

Miranda, Eduardo Reck; Shaji, Hari

doi:10.3390/app13042401

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…It is here that composers can experiment with different mapping designs [79,91]. An instance of a mapping designed for Qubism is presented below; please see [76] for more details, including the use of a two-dimensional PQCA.…”

Section: Pqca Musicmentioning

confidence: 99%

The advent of quantum computer music: mapping the field

Miranda

2024

Rep. Prog. Phys.

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Quantum computing technology is developing at a fast pace. The impact of quantum computing on the music industry is inevitable. This paper maps the emerging field of Quantum Computer Music. Quantum Computer Music investigates, and develops applications and methods to process music using quantum computing technology. The paper begins by contextualising the field. Then, it discusses significant examples of various approaches developed to date to leverage quantum computing to learn, process and generate music. The methods discussed range from rendering music using data from physical quantum mechanical systems and quantum mechanical simulations to computational quantum algorithms to generate music, including quantum AI. The ambition to develop techniques to encode audio quantumly for making sound synthesisers and audio signal processing systems is also discussed.

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Section: Pqca Musicmentioning

confidence: 99%

The advent of quantum computer music: mapping the field

Miranda

2024

Rep. Prog. Phys.

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“…Pure transformer models [43] have shown exceptional performance in video classification tasks, leveraging spatiotemporal token extraction techniques and regularization methods. The intersection of quantum computing and music generation was explored by the authors of [44], who delved into the use of partitioned quantum cellular automata for the creation of original music compositions, highlighting the potential impact of emerging quantum computing technology on the music industry. In the domain of music harmonization, the work reported in [45] introduced an innovative methodology for generating new harmonizations of jazz standards by combining melodies and chords from different songs.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

Garg,

Gajjar,

Shah

et al. 2023

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The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it relevant across academic and industrial domains. This paper presents a comprehensive comparison between standard deep learning architectures and emerging vision transformers, leveraging their success in various domains. We evaluate their performance on a specific subset of the GTZAN dataset, analyzing six different deep learning models. Our results demonstrate that DenseNet, a conventional deep learning architecture, achieves remarkable accuracy of 91.64%, outperforming vision transformers. However, we delve deeper into the analysis to shed light on the temporal characteristics of each deep learning model. Notably, the vision transformer and SWIN transformer exhibit a slight decrease in overall performance (1.82% and 2.29%, respectively), yet they demonstrate superior performance in temporal metrics compared to the DenseNet architecture. The significance of our findings lies in their contribution to the field of musical key estimation, where accurate and efficient algorithms play a pivotal role. By examining the strengths and weaknesses of deep learning architectures and vision transformers, we can gain valuable insights for practical implementations, particularly in music recommendation systems and automatic music transcription. Our research provides a foundation for future advancements and encourages further exploration in this area.

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Generative Music with Partitioned Quantum Cellular Automata

Cited by 2 publications

References 25 publications

The advent of quantum computer music: mapping the field

The advent of quantum computer music: mapping the field

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

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