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Erhu dataset for AI-Generated Music

The dataset enhances MIR tasks more easily by thoroughly understanding the erhu's acoustic fingerprint. Investigate the complexities of chord progressions, key changes, and tempo variations to improve your model's ability to accurately understand and classify musical patterns. Training models on this dataset enable the creation of generative AI systems capable of producing evocative and culturally rich music compositions inspired by the erhu's traditional sounds.

The erhu dataset provides an exclusive training environment for source separation applications. With ample data, your machine learning models can be trained to detect and isolate the unique sounds of the erhu, which will help enhance audio processing and extraction technologies. Harness the full potential of the erhu dataset for your machine learning projects, and venture into the world of generative AI music, MIR, source separation, and more with a resource that captures the rich tradition of the Chinese violin, pushing the boundaries of musical innovation.

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Dataset Highlights


Erhu Samples

The dataset includes a wide array of high-quality erhu samples, capturing various playing techniques, tones, and expressions. These samples serve as the foundation for AI models to grasp the intricacies of erhu music.

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Diverse Musical Genres

To ensure versatility, the dataset encompasses erhu performances across different musical genres. From classical to contemporary, traditional to experimental, the dataset exposes AI algorithms to a broad spectrum of erhu music styles.


Detailed Metadata

The dataset includes information about performers, recording conditions, and erhu specifications, is provided to offer context and additional insights for researchers and developers working with the dataset.


Data Integrity and Consistency

Rigorous quality control measures have been implemented to ensure the integrity and consistency of the dataset. This includes the removal of noise, normalization of audio levels, and meticulous annotation of musical elements.


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