Where can I find legally usable datasets for advanced audio chord recognition? [D]

The quest for high-quality, legally usable datasets for advanced audio chord recognition, as detailed in /u/DiscoramaMusic’s Reddit post, highlights a significant bottleneck in the burgeoning field of AI-powered music analysis. Building an engine capable of discerning the complex harmonic structures of jazz, soul, and film music – far beyond simple major/minor detection – demands a level of data sophistication currently lacking in readily available public resources. This challenge underscores a broader trend: the increasing need for specialized, expertly annotated datasets to fuel the next generation of AI models. The recent Hugging Face breach, [Hugging Face confirms breach affected internal datasets and credentials, urges users to take action], serves as a stark reminder of the fragility of data security and the importance of carefully vetting data sources, particularly when dealing with proprietary or commercially sensitive information. Similarly, Vijay Pande's shift towards smaller, more focused investments at VZVC, [“We’re not doing 30 bets a year”: Vijay Pande on betting small after running $4 billion at a16z], suggests a growing recognition within the AI investment landscape that deep, domain-specific expertise and data are often more valuable than broad, general-purpose models.

The user’s frustration with existing datasets—limited chord labels, weak annotations, and unsuitable repertoire—is entirely justified. Training a robust chord recognition model requires not just audio and basic chord labels, but also time alignment, beat/downbeat information, and a comprehensive chord vocabulary capable of representing nuanced harmonic progressions. The exploration of potential commercial datasets, or even the possibility of combining public resources with a privately licensed corpus, reflects the pragmatic reality of this challenge. The sheer volume of accurately annotated tracks needed—potentially hundreds, if not thousands—to achieve meaningful performance in jazz-influenced harmony represents a significant investment of time and resources. It’s a challenge that demands a shift in thinking beyond simply leveraging existing public data and towards a more curated, specialized approach. The mention of iReal Pro, Hooktheory, Ultimate Guitar, Chordify, and similar platforms raises crucial legal considerations. While these resources contain vast amounts of chord data, their usage for commercial model training is likely fraught with copyright and licensing complexities, making them generally unsuitable without explicit permission or licensing agreements.

The problem isn't just about the *existence* of data, but its *quality* and *accessibility*. Current MIR (Music Information Retrieval) research often falls short in providing the level of detail required for truly sophisticated chord recognition. The user’s call for practical, legally usable data sources and a plea for experience from those who have already navigated this space highlights a clear gap in the ecosystem. A collaborative effort, perhaps involving music educators, professional musicians, and AI researchers, could be instrumental in creating and curating datasets that meet the demands of advanced chord recognition models. This could involve creating standardized annotation guidelines, developing tools for efficient data annotation, and establishing clear licensing frameworks that allow for both research and commercial use.

Ultimately, the pursuit of high-fidelity audio-to-chord recognition represents a crucial step towards unlocking deeper insights into music composition and understanding. As AI continues to permeate the music industry, the availability of reliable and legally sound datasets will be paramount. What’s the long-term impact of increasingly sophisticated AI tools on music education and the development of musical skills? Will these tools augment human creativity, or will they risk homogenizing musical styles by relying on patterns learned from existing datasets?