Anechoic and IR Convolution-based Auralization Data Compilation Ensemble (AIRCADE)

AIRCADE is a data-compilation ensemble, primarily intended to serve as a resource for researchers in the field of dereverberation, particularly for data-driven approaches. It comprises speech and song samples, together with acoustic guitar sounds, with original annotations pertinent to emotion recognition and Music Information Retrieval (MIR). Moreover, it includes a selection of Impulse Response (IR) samples with varying Reverberation Time (RT) values, providing a wide range of conditions for evaluation. This data-compilation can be used together with provided Python scripts (available on GitHub), for generating auralized data ensembles in different sizes: tiny, small, medium and large. Additionally, the provided metadata annotations also allow for further analysis and investigation of the performance of dereverberation algorithms under different conditions. All data is licensed under Creative Commons Attribution 4.0 International License.

About the sizeable versions:

The data-compilation is hosted here at Zenodo, with an approximate total file size of 1.3 GB. For simplicity, all samples in our data-compilation were renamed, e.g., guitar_0000, rir_0000, song_0000, speech_0000, and so on. The ensemble versions are available in different sizes, from a tiny version, with limited data, to a large version, with almost 300,000 samples. This allows users to choose the most suitable version for their specific research needs. The following table illustrates the differences between all versions, detailing the number of song, speech, guitar, IR and auralized samples in each one, together with their respective total file size and duration.

For more information, please refer to our data paper on ArXiv.

Citation:

If you find AIRCADE useful in your research, please cite:

@misc{chiodi2023aircade,
    title={AIRCADE: an Anechoic and IR Convolution-based Auralization Data-compilation Ensemble},
    author={Túlio Chiodi and Arthur dos Santos and Pedro Martins and Bruno Masiero},
    year={2023},
    eprint={2304.09318},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}

Acknowledgement:

This work was partially supported by the São Paulo Research Foundation (FAPESP), grants #2017/08120-6 and #2019/22795-1.