Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas.

Instructions: 

Neural Audio Fingerprint Dataset

(c) 2021 by Sungkyun Chang

https://github.com/mimbres/neural-audio-fp

 

This dataset includes all music sources, background noises and impulse-reponses (IR) samples that have been used in the work "Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning" (https://arxiv.org/abs/2010.11910). 

This data set was generated by processing several external data sets, such as the Free Music Archive (FMA), Audioset, Common voice, Aachen IR, OpenAIR, Vintage MIC and the internal data set from Cochelar.ai. See README.md for details.

Dataset-mini vs. Dataset-full: the only difference between these two datasets is the size of 'test-dummy-db'.  So you can first train and test with `Dataset-mini`. `Dataset-full` is for  testing in 100x larger scale.

 

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The given Dataset is record of different age group people either diabetic or non diabetic for theie blood glucose level reading with superficial body features like body temperature, heart rate, blood pressure etc.

The main purpose of the dataset is to understand the effect of blood glucose level on human body. 

The different superficial body parameters show sifnificant variation according to change in blood glucose level.

Instructions: 

The use of dataset to be done for machine learning analysis or study purpose only. No medical implementations to be claimed using the given dataset.

 

 

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844 Views

The data of machine learning attacks for MF-PUF

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This dataset contains satellite images of areas of interest surrounding 30 different European airports. It also provides ground-truth annotations of flying airplanes in part of those images to support future research involving flying airplane detection. This dataset is part of the work entitled "Measuring economic activity from space: a case study using flying airplanes and COVID-19" published by the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. It contains modified Sentinel-2 data processed by Euro Data Cube.

Instructions: 

Details regarding dataset collection and usage are provided at https://github.com/maups/covid19-custom-script-contest

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278 Views

Parallel fractional hot-deck imputation (P-FHDI) is a general-purpose, assumption-free tool for handling item nonresponse in big incomplete data by combining the theory of FHDI and parallel computing. FHDI cures multivariate missing data by filling each missing unit with multiple observed values (thus, hot-deck) without resorting to distributional assumptions. P-FHDI can tackle big incomplete data with millions of instances (big-n) or 10, 000 variables (big-p).

Instructions: 

This repository includes three types of data: incomplete data with massive instances (big-n data), incomplete data with many variables (big-p data), incomplete data with tremendous instances and high dimensionality (ultra data). The repository has synthetic data and practical data from various scientific domains. Overall, there exist seven big-n datasets, four big-p datasets, and ten ultra datasets. For instructions, see Readme files in the dataset folder for the step-by-step use of UP-FHDI with different types of incomplete datasets.

 

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Context

This dataset consists of subject wise daily living activity data, which is acquired from the inbuilt accelerometer and gyroscope sensors of the smartphones.

Content

The smartphone was mounted on the waist and front pockets of the users. All the different activities were performed in a laboratory except Running, which was performed on a Football Playground.

Smartphone used: Poco X2 and Samsung Galaxy A32s

Inbuild Sensors used: Accelerometer and Gyroscope

Ages: All subjects are Above 23 years

Instructions: 

Smartphone used: Poco X2 and Samsung Galaxy A32s Inbuild Sensors used: Accelerometer and Gyroscope Ages: All subjects are Above 23 years Weight: All subjects are above 50 kgs No. of Subjects= 1

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Packet delivery ratio data collected for the article Wireless-Sensor Network Topology Optimization in Complex Terrain: A Bayesian Approach. Published in the IEEE Internet of Things Journal. 

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The data set contains inspections conducted by the Norwegian Labour Inspection Authority (NLIA) between 2012 and 2019. Each row in the dataset contains a control point, non-compliance indicator for the control point and industry code / municipality / county of the inspected organisation.

Instructions: 

Target label: Non-Compliance.

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<p>This is the image dataset for satellite image processing&nbsp; which is a collection therml infrared and multispectral images .</p>

Instructions: 

Dataset images
Thermal infrared images and multispectral images
image size:512x512
format:
image:.tiff
file :.h5

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444 Views

The "RetroRevMatchEvalICIP16" dataset provides a retrospective reviewer recommendation dataset and evaluation for IEEE ICIP 2016. The methodology via which the recommendations were obtained and the evaluation was performed is described in the associated paper. 

IMPORTANT: Currently only partial sample data is provided. Upon formal publication of the associated paper the dataset will be updated to the complete version.

              

Instructions: 

 Download the zip file and unzip to extract individual files. See the README.md file for details on what is included in the individual files.

                

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