*.wav
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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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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video dataset for the paper named "Analysis of ENF Signal Extraction From Videos Acquired by Rolling Shutters"
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This data set contains information on cardiopulmonary signals that were recorded simultaneously. The signals are separated into two folders, one titled heart sounds and the other lung sounds. In addition, two matlab programs are included, one with which the signals can be recorded and another to make graphs in time and frequency. It also has a pdf file that details the nomenclature of the signals.
This data set can be useful for various signal processing algorithms: filtering, PCA, LDA, ICA, CNN, etc.
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In-house raw audio data is collected from road traffic areas in Durgapur, a sub-urban city in India. We have developed a customized android application for collecting the data from the environment. Approximately 124km road traffic area is covered to do the same. The android application helps us to monitor the noise data in different sampling rates and bit rates. The sampling rate can be set to one of 8000 Hz, 16 000 Hz, 32 000 Hz, 44 100 Hz, 48 000 Hz. On the other hand, the bit rate can be set to one of 24 kbps, 48 kbps, 96 kbps, 128 kbps, 192 kbps.
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Asthma is a common, usually long-term respiratory disease with negative impact on society and the economy worldwide. Treatment involves using medical devices (inhalers) that distribute medicationto the airways, and its efficiency depends on the precision of the inhalation technique. Health monitoring systems equipped with sensors and embedded with sound signal detection enable the recognition of drug actuation and could be powerful tools for reliable audio content analysis.
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