Audio piracy detection is increasingly complex in decentralised distribution settings, where mainstream approaches fail to ensure robustness, verifiability, or computational efficiency. Conventional Digital Rights Management (DRM) systems mainly enforce licensed access, but once content is copied or redistributed outside their control they offer little protection. Classical fingerprinting approaches such as MFCC based hashes can detect near-exact duplicates, yet they often fail under signal edits like pitch shifting, time stretching or equalisation. Deep learning embeddings improve robustness but demand heavy computation and centralised resources, making them less suitable for edge or decentralised deployments. These limitations call for a solution that is both edit resilient and verifiable. We propose HashWave, a blockchain-integrated perceptual hashing framework that combines robust audio fingerprinting with tamper-proof verification. The system fuses MFCC, chroma and chroma CENS, CQT, spectral contrast, and lightweight tempo/energy cues, applying operation-aware weighting via [Formula: see text] and constrained DTW for time-scale edits. Evaluated across GTZAN, FMA-A Dataset for Music Analysis, and MUSAN (SLR17) with over twenty signal-processing transformations, HashWave achieves AUC 0.957 and TPR@1%FPR 0.952, outperforming MFCC-only baselines and approaching deep embeddings at lower CPU cost. The blockchain layer, built on Ethereum and IPFS, ensures decentralised hash storage, duplication control, and verifiable authorship with average upload and contract execution times of 0.017 s and 0.044 s. Together, these results establish HashWave as a practical, scalable, and secure framework for piracy detection across streaming, podcasting, and Web3 ecosystems.
Open access
Advanced Steganography and Watermarking Techniques
Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this profitable market. However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, we propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts. A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.
The purposes are to recognize and classify different music characteristics and strengthen the copyright protection system for original digital music in the big data era. Deep learning (DL) and blockchain technology are applied and researched herein. Based on CNN (Convolutional Neural Network), a music recognition method combined with hashing learning is proposed. The error generated when outputting the binary hash code is considered, and the semantic similarity of the hash code is ensured. Besides, the application of blockchain technology in the current intellectual property protection in original music is discussed. According to digital music property rights protection needs, the system is divided into modules, and its functions are designed. The system ensures its various functions by applying the application protocol designed in the Algor and network. In the experiments, the MagnaTagATune dataset is selected to verify the performance of the proposed CRNNH (Convolutional Recurrent Neural Network Hashing) algorithm. The algorithm shows the best music recognition performance under different bit numbers. When the number of connections is about 100, the QPS value of the blockchain-based music property rights protection system can be stabilized at about 20,000. At any number of threads, the system pressure will increase dramatically with the increase in the number of analog connections. The music recognition algorithm based on DL and hash method discussed is of great significance in improving the classification accuracy of music recognition. The application of blockchain technology in the copyright protection platform of original music works can protect the copyright of digital music and ensure the operation performance of the system.
Open access
Music and Audio Processing
Diverse Musicological Studies
Generative Adversarial Networks and Image Synthesis
Marcello Messina, Marcos Célio Filho, Carlos Mario Gómez Mejía, Damián Keller · 6 authors
We introduce IoMuSt — the Internet of Musical Stuff: a proposal to recalibrate the Internet of Musical Things in the light of the current reification of digital creative resources, epitomised by the Non-Fungible Tokens frenzy. As opposed to marketable “things”, “stuff ” is fluid, malleable, unfixable and pecuniarily irrelevant. Hence, stuff is good raw material for sustainable ubimus creative ecosystems
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.