This presentation will demonstrate using WebAssembly for audio plugins outside the web browser. Specifically, the showcase is a WebAssembly binary used as a synthesizer plugin in a commercial, desktop based Digital Audio Workstation software such as Garage Band or Logic. In addition there will be a proposal and demonstration of a commercial model for distributing and controlling access and ownership to these WebAssembly audio plugins, by storing them on the NEAR blockchain and binding them to Non-Fungible-Tokens ( NFTs ).
This paper examines the hesitancy of Australian musicians towards embracing the music non-fungible token (NFT) as a commodity. Drawing on the concepts of cultural autonomy and the digital disruptive sublime, the study argues that the overtly economic nature of NFTs challenges the ideology of creative independence in the hegemonic ‘indie’ music scene. Through interviews with nine Australian musicians who participated in our project, the research finds a cautious curiosity towards the technology, with technical barriers and a perceived cultural disconnect between the NFT ‘community’ and traditional music scenes contributing to hesitation. The paper concludes that attempts to engineer disruption in the music industry through web3/blockchain technology have thus far failed to attract sustained interest from musicians, as the cultural norms and practices associated with NFTs do not align with the values of the existing indie music ecosystem. The findings highlight the difficulties in planning and engineering cultural change within the music industry.
Non-fungible tokens (NFTs) are digital assets that provide unique ownership and authenticity of digital media such as art, music, and collectibles.NFT Marketplace is a blockchain-based platform that enables the creation, trading, and collecting of NFTs.The platform leverages blockchain technology to ensure the authenticity and ownership of NFTs, providing a secure and transparent way to transact digital assets.In this major project report, we explore the NFT Marketplace and its underlying blockchain technology.We provide an overview of the platform's features, including the ability to tokenize any digital asset, create customizable smart contracts, and sell NFTs with low fees and instant trades.We also discuss the advantages and disadvantages of the platform, including its ease of use, potential for fraud, and scalability challenges.The Non-Fungible Tokens (NFTs) have revolutionized the digital realm, redefining the concept of ownership and trade of unique digital assets.NFTs represent one-of-a-kind tokens, each verifiably and indelibly linked to a specific digital or physical asset, encompassing diverse forms of content, including video, audio, and images.These unique tokens have paved the way for creators to monetize their digital creations while providing collectors with an innovative means to invest, trade, and showcase their multimedia NFT collections.Central to the NFT ecosystem are NFT marketplaces, digital platforms designed to facilitate the creation, sale, and management of NFTs in various multimedia formats.These marketplaces have proliferated, providing creators with the means to mint NFTs, buyers with the opportunity to acquire them, and collectors with platforms to curate and trade their diverse NFT portfolios.We explore the multifaceted world of NFT marketplaces, focusing on their pivotal role in the creation, sale, and management of video, audio, and image NFTs.We analyze the economic implications, including pricing strategies and royalties, while addressing environmental sustainability concerns associated with NFTs.Challenges and opportunities encountered within this dynamic ecosystem are critically examined, including scalability, intellectual property rights, and the emergence of decentralized NFT marketplaces.Through in-depth case studies, we offer insights into the unique features and innovative approaches adopted by leading NFT marketplaces, shedding light on the transformative potential of this digital metaverse.This report serves as a valuable resource for those seeking a comprehensive understanding of NFT marketplaces catering to video, audio, and image NFTs, emphasizing the profound impact these tokens have on the creation, trade, and experience of digital content across various media formats.Navigating this dynamic digital frontier necessitates a nuanced perspective, and our survey aims to provide a holistic view of this rapidly evolving landscape.
Digital arts have gained an unprecedented level of popularity with the emergence of non-fungible tokens (NFTs). NFTs are cryptographic assets that are stored on blockchain networks and represent a digital certificate of ownership that cannot be forged. NFTs can be incorporated into a smart contract which allows the owner to benefit from a future sale percentage. While digital art producers can benefit immensely with NFTs, their production is time consuming. Therefore, this paper explores the possibility of using generative adversarial networks (GANs) for automatic generation of digital arts. GANs are deep learning architectures that are widely and effectively used for synthesis of audio, images, and video contents. However, their application to NFT arts have been limited. In this paper, a GAN-based architecture is implemented and evaluated for novel NFT-style digital arts generation. Results from the qualitative case study indicate that the generated artworks are comparable to the real samples in terms of being interesting and inspiring and they were judged to be more innovative than real samples.
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Generative Adversarial Networks and Image Synthesis
To explore the automatic computer composition, investigate the copyright protection and management of digital music, and expand the application of deep learning and blockchain technologies in the generation of digital music works, piano composition was taken as a sample. First, through the elaboration of the neural network methods based on deep learning, the Recurrent Neural Network (RNN), Long-Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) networks were introduced, and the deep learning-based GRU-RNN automatic composition model was constructed. Second, the blockchain technology was analyzed and expressed, and the problems in the traditional copyright protection and management of digital music were analyzed. The three aspects, i.e., ownership, right of use, and right protection, were fully considered, and the blockchain technology was integrated into the copyright protection and management of digital music. Finally, the manual analysis evaluation and pause analysis were selected as the indicators to analyze and characterize the music composition quality of the GRU-RNN model, as well as analyzing the development of the digital music market integrated with blockchain technology. The results show that the GRU-RNN model shows satisfactory effects in manual analysis evaluation or in the pause analysis of the passage. The deep learning method has great potential for application in automatic computer composition of digital music; the integration of blockchain technology has played a promotive role in the expansion and popularization of the digital music market. However, in the meantime, it still faces some technical and policy challenges. The results have a positive effect on promoting the development and application of deep learning methods and blockchain technology in digital music.
In order to explore the application of deep learning algorithms in arrangement and composition, and the role of blockchain in the protection of digital music copyright, a monophonic melody composition model based on the deep generative adversarial networks (DCGANs) is constructed firstly, and the composition performance of the model is analyzed using hymn as input sample in this study. Later, the multi-instrument co-arrangement (MICA) model based on the multi-task learning is proposed, and the composition performance is analyzed by taking the actual music as an input sample. Finally, the improved practical byzantine fault tolerance (IPBFT) algorithm is proposed, and a digital music copyright protection system is designed based on the blockchain in this study. The results indicate that the accuracies constructed DCGANs model in predicting the Soprano and Alto voice melody are higher than those of the DeepBatch model by 2.29% and 3.32%, respectively. The performance on the harmony score, note accuracy, Levenshtein similarity (LS), notes distribution mean square error, and empty as well as the convergence speed of the constructed MICA model are better than those of other models. The average transaction per second (TPS) value of the proposed IPBFT algorithm in the real digital music copyright protection system is 3469, which is superior to other blockchain technologies. Finally, the digital music copyright protection system is achieved, the error rate of completing the request is 0% in the state of many users operating concurrently, and a high TPS value can be guaranteed. In short, the DCGANs and MICA models pointed out in this study can be used in the composition of monophonic melodies and complex melodies, and the digital music copyright protection system based on the blockchain has excellent performance in practical applications.
Open access
Generative Adversarial Networks and Image Synthesis