Enterprise

This is a tracked list of every major AI music copyright lawsuit filed since the generative AI boom began. It is maintained for ML teams, legal counsel, investors, and journalists who need to understand the legal landscape without wading through every case filing. Last updated April 9, 2026. The list is organized by case and includes filing date, court, parties, statute, damages sought, current status, and any settlement terms. Sources are linked where available. If a case is not on this list, it either (a) has not been filed or (b) is below the threshold of commercial relevance for the...

This is a report on the state of vocal data licensing in 2026. It is written for ML teams, legal counsel, investors, and commercial operators trying to understand where the market is, how it got here, and what is coming next. The data is drawn from primary legal sources, publicly disclosed commercial deals, and direct observation of the market from inside The Vocal Market's enterprise program. If you read only one section, read the summary below. If you have time for more, the sections after it provide the context and specifics behind each of the headline findings. Summary: what changed...

Vocal dataset licensing is a young enough market that published price lists are rare and comparable transactions are rarer. Buyers who want a price-comparison dashboard are out of luck. What they can do instead is understand the variables that drive cost, the ranges that are typical for different use cases, and the questions that matter during commercial negotiation. This post covers all three. We are not going to publish a price list. We will not be the last vendor to avoid publishing one. The reason is not that vendors are hiding information; it is that vocal dataset pricing is genuinely...

Before we built The Vocal Market's enterprise licensing program, the default answer to "where do I get vocal training data?" for most ML teams was "start with the academic datasets." MUSDB18, OpenCpop, VCTK, OpenSinger, M4Singer, and a handful of others are freely available, well-documented, and widely used in published research. They are also, in almost every case, not actually usable for commercial AI training. Not because the data is bad, but because the licenses that attach to the data either prohibit commercial use entirely or impose restrictions that make commercial use legally fragile. This post walks through the major academic...

You have decided to license a vocal dataset rather than build one. Now you have to choose a vendor. The market has grown enough that there are multiple vendors to evaluate, but it has not grown enough for the vendor landscape to be well understood or standardized. Each vendor will describe their offering in their own terms, each will downplay the weaknesses you are trying to uncover, and each will pressure you to move fast. This post is a 12-question framework for evaluating vocal data vendors. It is designed so that the answers let you compare vendors against each other...

Every ML team building a voice or music model eventually runs the build-vs-buy analysis on training data. The question is whether it is better to record your own vocal dataset in-house, giving you complete control but requiring significant infrastructure and time, or to license an existing commercial dataset, trading control for speed and scale. The answer depends on specifics: your budget, your timeline, the quality ceiling you need, and the unique characteristics of your use case. This post walks through the actual numbers and trade-offs so you can run the analysis for your own team. The build option: what it...