Helsinki Speech Challenge 2024

The Helsinki Speech Challenge 2024 (HSC2024) invites researchers to enhance and deconvolve speech audio recordings. We recorded a dataset that challenges participants to apply speech enhancement and inverse problems techniques to recorded speech data. This dataset includes paired samples of AI-generated clean speech and corresponding recordings, which feature varying levels of corruption, including frequency attenuation and reverberation. The challenge focuses on developing innovative deconvolution methods to accurately recover the original audio. The effectiveness of these methods will be quantitatively assessed using a speech recognition model, providing a relevant metric for evaluating enhancements in real-world scenarios.

Helsinki Deblur Challenge 2021: description of photographic data

The photographic dataset collected for the Helsinki Deblur Challenge 2021 (HDC2021) contains pairs of images taken by two identical cameras of the same target but with different conditions. One camera is always in focus and produces sharp and low-noise images the other camera produces blurred and noisy images as it is gradually more and more out of focus and has a higher ISO setting. Even though the dataset was designed and captured with the HDC2021 in mind it can be used for any testing and benchmarking of image deblurring algorithms. The data is available here: https://doi.org/10.5281/zenodo.477228