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DESED dataset is the dataset that was used in DCASE 2019 task 4. The dataset for this task is composed of 10 sec audio clips recorded in domestic environment or synthesized using Scaper to simulate a domestic environment.
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EAED is an Egyptian-Arabic emotional speech dataset containing 3,614 audio files. The dataset is a semi-natural one as it was collected from five well-known Egyptian TV series. Each audio file ranged in length from 1 to 8 seconds depending on the completion time of the given sentence.
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The dataset is collected from the xeno-canto website, which is a public website to share bird sounds from around the world . We first collect 15,300 bird sounds from one second to fifteen seconds. Unlike many audio denoising datasets which have manually added artificial noise, our collected bird sounds contain natural noises, including wind, waterfall, rain, etc.
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SLCeleb
Here we collected data through social media such as Youtube, because the best method to obtain data from a variety of wild and diverse acoustic environments is to use a freely available source. Otherwise, manually creating such volatility would take a long time. Even after that, we will not be able to share the data collected with other researchers.
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The dataset consists of acoustic signals acquired from the surface of the knee of 14 subjects. The description of study group and methodology of the experiment can be found in the publication: https://doi.org/10.3390/s21196495.
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We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson-Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions.
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We present Vocal92, a multivariate Cappella solo singing and speech audio dataset spanning around 146.73 hours sourced from volunteers. To the best of our knowledge, this is the first dataset of its kind that specifically focuses on a cappella solo singing and speech. Furthermore, we use two current state-of-the-art models to construct the singer recognition baseline system.
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Touch-screens are the basic and convenient human-computer interface. They are extensively used in digital musical applications, where a complex action-perception loop is involved. Therefore, it is crucial to establish a rich vibrotacticle feedback to improve the quality of the user's interaction. This paper explores the capacity of Generative Adversarial Networks (GANs) to generate time-reversed signals that can achieve localized vibrotactile feedback on a rigid surface.
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A video dataset for the paper named "Analysis of ENF Signal Extraction From Videos Acquired by Rolling Shutters" submitted to IEEE Transactions on Information Forensics and Security (T-IFS) and under review.
If you used our dataset, please cite our paper as:
Jisoo Choi, Chau-Wai Wong, Hui Su, and Min Wu, "Analysis of ENF signal extraction from videos acquired by rolling shutters," submitted to IEEE Transactions on Information Forensics and Security (T-IFS), under review.
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