AI-generated music is flooding streaming platforms. AI-generated music is flooding streaming platforms. 'Music needs a human component to be of any value': Guardian readers on the growing use of AI in music AI promises to have far-reaching effects in music-making. While some welcome it as a compositional tool, many have deep concerns. A I-generated music is flooding streaming platforms, and it seems to be here to stay.

The rapid rise of AI-generated art has sparked debate about potential biases in how audiences perceive and evaluate such works. This study investigates how composer information and listener characteristics shape the perception of AI-generated music, adopting a mixed-method approach. Using a diverse set of stimuli across various genres from two AI music models, we examine effects of perceived authorship on liking and emotional responses, and explore how attitudes toward AI, personality traits, and music-related variables influence evaluations. We further assess the influence of perceived humanness and analyze open-ended responses to uncover listener criteria for judging AI-generated music. Attitudes toward AI proved to be the best predictor of both liking and emotional intensity of AI-generated music. This quantitative finding was complemented by qualitative themes from our thematic analysis, which identified ethical, cultural, and contextual considerations as important criteria in listeners' evaluations of AI-generated music. Our results offer a nuanced view of how people experience music created by AI tools and point to key factors and methodological considerations for future research on music perception in human-AI interaction.

Warner Music Group (WMG) will begin an artificial intelligence (AI) music venture with technology start-up Suno - a year after it sued the firm in a landmark case. As part of the settlement agreement struck between the two firms, Warner will let users create AI-generated music on Suno using the voices, names and likeness of artists who opt-in to the programme. The record label, which represents artists like Dua Lipa, Coldplay and Ed Sheeran, was among several music giants like Sony Music that sued Suno and a similar platform called Udio. AI-generated content has been controversial, with many artists voicing concerns that it could undermine human songwriters. Starting next year, Suno will roll out new advanced and licensed models to its generative-AI music platform, which allows users to create music based on simple descriptions, said Warner in a statement .

Walk My Walk, Livin' on Borrowed Time and We Say No, No, No to an Asylum Center topped Spotify's charts this week. Walk My Walk, Livin' on Borrowed Time and We Say No, No, No to an Asylum Center topped Spotify's charts this week. Three songs generated by artificial intelligence topped music charts this week, reaching the highest spots on Spotify and Billboard charts. Walk My Walk and Livin' on Borrowed Time by the outfit Breaking Rust topped Spotify's "Viral 50" songs in the US, which documents the "most viral tracks right now" on a daily basis, according to the streaming service. A Dutch song, We Say No, No, No to an Asylum Center, an anti-migrant anthem by JW "Broken Veteran" that protests against the creation of new asylum centers, took the top position in Spotify's global version of the viral chart around the same time.

They went viral, amassing more than 1m streams on Spotify in a matter of weeks, but it later emerged that hot new band the Velvet Sundown were AI-generated – right down to their music, promotional images and backstory. The episode has triggered a debate about authenticity, with music industry insiders saying streaming sites should be legally obliged to tag music created by AI-generated acts so consumers can make informed decisions about what they are listening to. Initially, the "band", described as "a synthetic music project guided by human creative direction", denied they were an AI creation, and released two albums in June called Floating On Echoes and Dust And Silence, which were similar to the country folk of Crosby, Stills, Nash & Young. Things became more complicated when someone describing himself as an "adjunct" member told reporters that the Velvet Sundown had used the generative AI platform Suno in the creation of their songs, and that the project was an "art hoax".

The recent rise in capabilities of AI-based music generation tools has created an upheaval in the music industry, necessitating the creation of accurate methods to detect such AI-generated content. This can be done using audio-based detectors; however, it has been shown that they struggle to generalize to unseen generators or when the audio is perturbed. Furthermore, recent work used accurate and cleanly formatted lyrics sourced from a lyrics provider database to detect AI-generated music. However, in practice, such perfect lyrics are not available (only the audio is); this leaves a substantial gap in applicability in real-life use cases. In this work, we instead propose solving this gap by transcribing songs using general automatic speech recognition (ASR) models. We do this using several detectors. The results on diverse, multi-genre, and multi-lingual lyrics show generally strong detection performance across languages and genres, particularly for our best-performing model using Whisper large-v2 and LLM2Vec embeddings. In addition, we show that our method is more robust than state-of-the-art audio-based ones when the audio is perturbed in different ways and when evaluated on different music generators. Our code is available at https://github.com/deezer/robust-AI-lyrics-detection.

Up to seven out of 10 streams of artificial intelligence-generated music on the Deezer platform are fraudulent, according to the French streaming platform. The company said AI-made music accounts for just 0.5% of streams on the music streaming platform but its analysis shows that fraudsters are behind up to 70% of those streams. AI-generated music is a growing problem on streaming platforms. Fraudsters typically generate revenue on platforms such as Deezer by using bots to "listen" to AI-generated songs – and take the subsequent royalty payments, which become sizeable once spread across multiple tracks. The tactic aims to evade detection measures triggered by vast listening numbers for a small amount of bogus tracks.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. Scarlett Johansson has taken a vocal stand on artificial intelligence, after having her likeness and voice used without permission. Last year, Johansson said she had been asked to voice OpenAI's Chatbot by CEO Sam Altman, but turned down the job, only for people to notice that the feature, named "Sky," sounded almost exactly like the actress. It was like: If that can happen to me, how are we going to protect ourselves from this? There's no boundary here; we're setting ourselves up to be taken advantage of," the 40-year-old told InStyle Magazine earlier this month. In a statement to NPR following the release of "Sky," Johansson said, "When I heard the released demo, I was shocked, angered and in disbelief that Mr. Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news outlets could not tell the difference.

In recent years, artificial intelligence (AI) has made significant progress in the field of music generation, driving innovation in music creation and applications. This paper provides a systematic review of the latest research advancements in AI music generation, covering key technologies, models, datasets, evaluation methods, and their practical applications across various fields. The main contributions of this review include: (1) presenting a comprehensive summary framework that systematically categorizes and compares different technological approaches, including symbolic generation, audio generation, and hybrid models, helping readers better understand the full spectrum of technologies in the field; (2) offering an extensive survey of current literature, covering emerging topics such as multimodal datasets and emotion expression evaluation, providing a broad reference for related research; (3) conducting a detailed analysis of the practical impact of AI music generation in various application domains, particularly in real-time interaction and interdisciplinary applications, offering new perspectives and insights; (4) summarizing the existing challenges and limitations of music quality evaluation methods and proposing potential future research directions, aiming to promote the standardization and broader adoption of evaluation techniques. Through these innovative summaries and analyses, this paper serves as a comprehensive reference tool for researchers and practitioners in AI music generation, while also outlining future directions for the field.

Recent advancements in music generation are raising multiple concerns about the implications of AI in creative music processes, current business models and impacts related to intellectual property management. A relevant discussion and related technical challenge is the potential replication and plagiarism of the training set in AI-generated music, which could lead to misuse of data and intellectual property rights violations. To tackle this issue, we present the Music Replication Assessment (MiRA) tool: a model-independent open evaluation method based on diverse audio music similarity metrics to assess data replication. We evaluate the ability of five metrics to identify exact replication by conducting a controlled replication experiment in different music genres using synthetic samples. Our results show that the proposed methodology can estimate exact data replication with a proportion higher than 10%. By introducing the MiRA tool, we intend to encourage the open evaluation of music-generative models by researchers, developers, and users concerning data replication, highlighting the importance of the ethical, social, legal, and economic consequences. Code and examples are available for reproducibility purposes.