In a fragmented and fast-moving media landscape, establishing a reliable view of music usage is essential for fair compensation. Differences between expectation and actual usage can arise easily, particularly across multiple platforms and formats. By applying Multilayer Music Recognition (MMR), SoundAware provides a consistent and objective foundation for understanding music usage, supporting transparency and trust across the ecosystem.
The music industry is built on creativity, by composers, lyricists, publishers, and many others. At the same time, it depends on accurate information to ensure that creators are recognised and rewarded for their work.
SoundAware focuses on identifying music usage at scale, across a wide range of environments, providing the data that enables our customers to ensure that rights holders are compensated correctly. Let us illustrate this with a real-world example.
A composer, whom we will call Chris Omposer (CO), contacted us with a concern:
CO: “I created music for a game show, but I wasn’t paid for one of the episodes. How is that possible?”
We fully understand the importance of such questions. Accurate attribution is essential. We therefore looked into the details:
SoundAware: “When was the usage, and on which channel or platform?”
CO: “Somewhere in August, on TV1.”
SoundAware: “And were all sound files submitted?”
CO: “Yes, as always.”
After analysing the available data, we found that CO’s music was not used in that specific episode. It was a themed programme in which other music had been selected. We shared our findings with CO. While initially unexpected, the conclusion became clear when reviewing parts of the content together. Human memory can be selective, especially in a dynamic production environment.
This example highlights an important aspect of modern music recognition. By combining multiple analytical layers and data sources, we are able to establish a reliable and objective view of music usage.
Through Multilayer Music Recognition, SoundAware integrates audio fingerprinting, melody recognition, lyrics matching, and Gen-AI music detection within a single analytical framework. Where relevant, AI supports specific parts of the analysis, while human expertise remains essential for quality assurance and control. This approach ensures that identification is both accurate and comprehensive across different types of content and usage scenarios.
Whether music is used in broadcast, online video, social media, or physical spaces, a consistent and reliable understanding of usage is essential. At the same time, collaboration remains important. Rights holders benefit from providing complete and accurate data, while relying on robust systems to reflect actual usage.
In an increasingly diverse media landscape, establishing a clear and reliable picture of music usage is more important than ever. By combining advanced technology with human expertise, SoundAware contributes to transparency, trust, and fair compensation across the entire ecosystem.