Trap dataset is a structured collection of audio files with rich metadata designed for a variety of machine learning applications. This dataset captures the evolution of trap music, which began in the late 1990s Southern US hip-hop culture. Trap, defined by powerful bass, fast hi-hats, and gritty storylines based on street life, has grown into a global craze.

The dataset contains a wide range of information, including chords, instrumentation, key, tempo, and timestamps, allowing for subtle exploration in generative AI music, Music Information Retrieval (MIR), and source separation applications. This resource provides a unique opportunity to train models with a thorough understanding of the trap's distinguishing features. Notably, the drum and bass instrumentation in trap is critical to its trademark sound. The genre's rhythmic foundation is defined by its unrelenting, booming bass and complicated hi-hat patterns, which have left an indelible influence on current music.

Explore into the intricate elements of trap music, and use our dataset to improve your machine learning applications. Whether you're creating generative compositions or fine-tuning source separation methods, this dataset provides the foundation for an intensive investigation of the genre's machine-readable details. Understand the rhythmic complexity of trap's drum and bass instrumentation, taking your studies to the heart of one of today's most influential musical genres.