Tootfinder

AI bots playing music for other bots. A signpost along the way to our Matrix-like future
https://futurism.com/ai-band-spotify-streams

An "Indie Rock Band" That Appears to Be Entirely AI-Generated Is Making Alarming Amounts of Money on Spotify
While real artists struggle to earn money on Spotify, a seemingly AI-generated band has garnered enough streams to actually make a buck. 

The music industry is developing tools to detect AI-generated music across the music pipeline, with a focus on licensing and control rather than takedowns (Jack Buehrer/The Verge)
https://www.theverge.com/ai-artificial-int

The music industry is building the tech to hunt down AI songs
With no way to stop the onslaught of AI music, the industry is taking a different approach: figuring out how to make money off of it.

The music industry is developing tools to detect AI-generated music across the music pipeline, with a focus on licensing and control rather than takedowns (Jack Buehrer/The Verge)
https://www.theverge.com/ai-artificial-int

The music industry is building the tech to hunt down AI songs
With no way to stop the onslaught of AI music, the industry is taking a different approach: figuring out how to make money off of it.

Deezer rolls out AI tagging system to fight streaming fraud; says up to 70% of streams from fully AI-generated tracks are fraudulent
#AI #MusicTech #NotTrustworthy

Deezer rolls out AI tagging system to fight streaming fraud; says up to 70% of streams from fully AI-
Platform has introduced what it claims to be the world’s first AI tagging system for music streaming…

Deezer rolls out AI tagging system to fight streaming fraud; says up to 70% of streams from fully AI-generated tracks are fraudulent

Deezer rolls out AI tagging system to fight streaming fraud; says up to 70% of streams from fully AI-
Platform has introduced what it claims to be the world’s first AI tagging system for music streaming…

AI-Generated Song Detection via Lyrics Transcripts
Markus Frohmann, Elena V. Epure, Gabriel Meseguer-Brocal, Markus Schedl, Romain Hennequin
https://arxiv.org/abs/2506.18488

AI-Generated Song Detection via Lyrics Transcripts
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. Howe…

Deezer says AI-made music accounts for just 0.5% of streams on the music streaming platform, but up to 70% of those streams are fraudulent (Dan Milmo/The Guardian)
https://www.theguardian.com/technology/202

i'm finna hike up to the used record store and see if i can sell some records
Machine-generated ALT: three photos showcase a collection of 12 diverse vinyl records, spanning genres such as rock, soul, jazz, Christmas music, and contemporary pop, featuring artists like Lucinda Williams, Frank Sinatra, Chick Corea, Ramsey Lewis, Lana Del Rey, Mavis Staples, and Hugh Masekela.

Exploring listeners' perceptions of AI-generated and human-composed music for functional emotional applications
Kimaya Lecamwasam, Tishya Ray Chaudhuri
https://arxiv.org/abs/2506.02856

Exploring listeners' perceptions of AI-generated and human-composed music for functional emotional applications
This work investigates how listeners perceive and evaluate AI-generated as compared to human-composed music in the context of emotional resonance and regulation. Across a mixed-methods design, participants were exposed to both AI and human music under various labeling conditions (music correctly labeled as AI- or human-origin, music incorrectly labeled as AI- or human-origin, and unlabeled music) and emotion cases (Calm and Upbeat), and were asked to rate preference, efficacy of target emotion …

A Fourier Explanation of AI-music Artifacts
Darius Afchar, Gabriel Meseguer-Brocal, Kamil Akesbi, Romain Hennequin
https://arxiv.org/abs/2506.19108 https:/…

A Fourier Explanation of AI-music Artifacts
The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringement, job displacement, and ethical implications have led to growing scrutiny and legal challenges. In parallel, AI-detection services have emerged, yet these systems remain largely opaque and privately controlled, mirroring the very issues they aim to address. This paper explores the fundamental properties of synthet…

Large-Scale Training Data Attribution for Music Generative Models via Unlearning
Woosung Choi, Junghyun Koo, Kin Wai Cheuk, Joan Serr\`a, Marco A. Mart\'inez-Ram\'irez, Yukara Ikemiya, Naoki Murata, Yuhta Takida, Wei-Hsiang Liao, Yuki Mitsufuji
https://arxiv.org/abs/2506.18312

Large-Scale Training Data Attribution for Music Generative Models via Unlearning
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed to the generation of a particular output from a specific model. This is crucial in the context of AI-generated music, where proper recognition and credit for original artists are generally overlooked. By enabling white-box attribution, our work supports a fairer system for acknowledg…

Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets
Xinyi Wu, Haohong Wang, Aggelos K. Katsaggelos
https://arxiv.org/abs/2506.07076

Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets
With the popularity of video-based user-generated content (UGC) on social media, harmony, as dictated by human perceptual principles, is critical in assessing the rhythmic consistency of audio-visual UGCs for better user engagement. In this work, we propose a novel harmony-aware GAN framework, following a specifically designed harmony evaluation strategy to enhance rhythmic synchronization in the automatic music-to-motion synthesis using a UGC dance dataset. This harmony strategy utilizes refin…

I remember a time when I called a business and someone answered. Now when the phone is answered, I am immediately put on hold to listen to annoying computer generated music, then an announcement about how much they value my business, then a land acknowledgement, then a warning about abusive language or behaviour, then a number select menu, which does not include “talk to the receptionist”, then I pick a number and have to leave a message! 🤷😠

Diff-TONE: Timestep Optimization for iNstrument Editing in Text-to-Music Diffusion Models
Teysir Baoueb, Xiaoyu Bie, Xi Wang, Ga\"el Richard
https://arxiv.org/abs/2506.15530 …

Diff-TONE: Timestep Optimization for iNstrument Editing in Text-to-Music Diffusion Models
Breakthroughs in text-to-music generation models are transforming the creative landscape, equipping musicians with innovative tools for composition and experimentation like never before. However, controlling the generation process to achieve a specific desired outcome remains a significant challenge. Even a minor change in the text prompt, combined with the same random seed, can drastically alter the generated piece. In this paper, we explore the application of existing text-to-music diffusion …

This https://arxiv.org/abs/2505.22238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval
We present Yambda-5B, a large-scale open dataset sourced from the Yandex.Music streaming platform. Yambda-5B contains 4.79 billion user-item interactions from 1 million users across 9.39 million tracks. The dataset includes two primary types of interactions: implicit feedback (listening events) and explicit feedback (likes, dislikes, unlikes and undislikes). In addition, we provide audio embeddings for most tracks, generated by a convolutional neural network trained on audio spectrograms. A key…

Improving AI-generated music with user-guided training
Vishwa Mohan Singh, Sai Anirudh Aryasomayajula, Ahan Chatterjee, Beste Aydemir, Rifat Mehreen Amin
https://arxiv.org/abs/2506.04852

Improving AI-generated music with user-guided training
AI music generation has advanced rapidly, with models like diffusion and autoregressive algorithms enabling high-fidelity outputs. These tools can alter styles, mix instruments, or isolate them. Since sound can be visualized as spectrograms, image-generation algorithms can be applied to generate novel music. However, these algorithms are typically trained on fixed datasets, which makes it challenging for them to interpret and respond to user input accurately. This is especially problematic beca…