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WavePulse: Real-time Content Analytics of Radio Livestreams

Mittal, Govind, Gupta, Sarthak, Wagle, Shruti, Chopra, Chirag, DeMattee, Anthony J, Memon, Nasir, Ahamad, Mustaque, Hegde, Chinmay

arXiv.org Artificial Intelligence

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.


Estimate the building height at a 10-meter resolution based on Sentinel data

Yan, Xin

arXiv.org Artificial Intelligence

Building height is an important indicator for scientific research and practical application. However, building height products with a high spatial resolution (10m) are still very scarce. To meet the needs of high-resolution building height estimation models, this study established a set of spatial-spectral-temporal feature databases, combining SAR data provided by Sentinel-1, optical data provided by Sentinel-2, and shape data provided by building footprints. The statistical indicators on the time scale are extracted to form a rich database of 160 features. This study combined with permutation feature importance, Shapley Additive Explanations, and Random Forest variable importance, and the final stable features are obtained through an expert scoring system. This study took 12 large, medium, and small cities in the United States as the training data. It used moving windows to aggregate the pixels to solve the impact of SAR image displacement and building shadows. This study built a building height model based on a random forest model and compared three model ensemble methods of bagging, boosting, and stacking. To evaluate the accuracy of the prediction results, this study collected Lidar data in the test area, and the evaluation results showed that its R-Square reached 0.78, which can prove that the building height can be obtained effectively. The fast production of high-resolution building height data can support large-scale scientific research and application in many fields.


Disgraced Donkey Kong Master Says He's Got Proof He Didn't Cheat

NPR Technology

Billy Mitchell poses for the media in Ottumwa, Iowa, in 2009. Billy Mitchell poses for the media in Ottumwa, Iowa, in 2009. Billy Mitchell has broken his silence. The longtime Donkey Kong champ, never one to stay quiet long, pushed back Sunday against official scorekeepers' decisions last week to strip him of his best-known video game records. Twin Galaxies, the organization that tracks such records, disqualified his high scores and banned him from competition after an investigation turned up evidence that several of his Donkey Kong scores were set on inappropriate hardware.