Rightsify’s GCX: Your Go-To Source for High-Quality, Ethically Sourced, Copyright-Cleared AI Music Training Datasets with Rich Metadata

Rightsify’s Global Copyright Exchange (GCX) offers vast collections of copyright-cleared music datasets tailored for machine learning and generative AI music initiatives.  These datasets encompass millions of hours of music, over 10 million recordings and compositions accompanied by comprehensive metadata, including key, tempo, instrumentation, keywords, moods, energies, chords, and more, facilitating training and commercial usage. 

Text, Stem, MIDI, and sheet music pairings for audio are bundled with their AI music datasets, furnishing comprehensive resources for ML projects. Also, their library features an expansive range of over 1000 instruments, representing diverse cultural heritages worldwide and promoting inclusivity and global representation in AI music models.

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Developing a robust AI for music necessitates vast, high-quality training data comprising diverse solo instruments and MIDI files. While scraping online sources may seem tempting, it risks copyright infringement lawsuits and devalues human music creators in addition to being low quality data with limited text pairs.  

Armed with a conscientiously assembled and extensively annotated dataset (Manual annotation by human music experts), AI music developers are primed to pioneer innovative products. Rightsify’s GCX manually annotates all musical data by a large team of experienced musicians, ensuring efficient research preprocessing and accurate labeling of text pairs across the dataset. 

Rightsify built a substantial database library by purchasing music rights from artists while cataloging its collection over its decade plus of being in business. This library systematically categorizes songs, capturing key, tempo, chords, instrumentation, song structures, time signature, genre and more.

Having absolute copyright ownership of the data, Rightsify offers indemnity to developers for employing the data in their models commercially. Furthermore, the datasets are accessible for research purposes and universities. 

GCX’s comprehensive music datasets enable large scale music models that elevate creativity and efficiency. From crafting personalized songs to seamlessly integrating background music into video content, their legally compliant and diverse datasets guarantee exceptional quality and commercially-safe AI models. These datasets also help improve source separation, genre classification, mood detection, music recommendation, synthetic data creation, and more.

Fig. Description of Piano Jazz Dataset by GCX

Four major use cases for Rightsify’s datasets are the following:

  1. AI Music Generation:

Music datasets containing a wide range of musical styles, genres, and compositions, enable the models to learn the intricate patterns, structures, and characteristics of music. By capturing the essence of melody, harmony, rhythm, and timbre, these datasets allow generative models to create novel and coherent musical compositions. Researchers and developers can leverage music datasets to push the boundaries of AI-assisted music creation, exploring new frontiers in computational creativity.

  1. Source Separation: 

Music datasets that include isolated stems for individual instruments and vocals are crucial for training models in audio source separation tasks. By providing a large volume of annotated and isolated audio samples, music datasets facilitate the training of robust source separation models. These models have applications in music production, audio post-production, and audio enhancement, allowing for precise manipulation and control over individual audio elements.

  1. Music Information Retrieval (MIR):

Music datasets with comprehensive metadata, including genre labels, instrumentation, key, tempo and other parameters, form the backbone of Music Information Retrieval research. These datasets enable the development of sophisticated models for various MIR tasks, such as genre classification, instrument identification, key and tempo identification, music emotion recognition, and lyrics analysis.

  1. Music Recommendation:

Music recommendation systems heavily rely on music datasets that contain detailed metadata. These datasets enable personalized recommendations based on musical characteristics. By exploiting the rich information present in music datasets, researchers can build sophisticated recommendation engines that offer personalized and context-aware music suggestions, enhancing user engagement and satisfaction.

Snapshot of Use Case Examples

Rightsify launched GCX in 2023 and created the data licensing framework for legally clean AI music. GCX provides datasets with over 4.4 million hours of audio and 32 billion metadata text pairs, totaling over 3 petabytes of music data. The perfect data source for training AI music models. Rightsify has granted licenses to six AI developers thus far, from startups to big tech companies. The team continually expands the dataset, adding thousands of new tracks and text pairs weekly.

An example of Rightsify’s GCX datasets

In conclusion, GCX is the leading data licensing provider fueling the realm of AI Music datasets. It facilitates the ethical licensing and copyright clearance of premium training datasets essential for AI-driven music endeavors. Spearheaded by Rightsify’s groundbreaking launch of the World’s 1st AI Music Dataset Licensing Service in 2023, GCX has pioneered a framework ensuring the legality of AI-generated music for commercial utilization. Its five essential aspects make it the World’s 01 choice for AI Music Datasets:

  • Ethically Sourced
  • Copyright Cleared
  • High-Quality Files
  • Rich Metadata
  • Unlimited Customization


Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

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