Aditya Dutt (AdityaDutt)

AdityaDutt

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Company:University of Florida

Location:Gainesville, Florida

Home Page:www.linkedin.com/in/adityadutt12

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Aditya Dutt's starred repositories

LiDAR-and-Hyperspectral-Fusion-classification

Landcover classification using the fusion of HSI and LiDAR data.

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scikit-plot

An intuitive library to add plotting functionality to scikit-learn objects.

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keras-triplet-loss

A simple keras port of omoindrot's tensorflow-triplet-loss repository.

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Autoencoders-and-decoders-using-keras-and-tensorflow

This repo contains auto encoders and decoders using keras and tensor flow. It shows the exact encoding and decoding with the code part.

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tensorflow_keras_color_images_denoiser

Removes gaussian noise from colored images using an autoencoder.

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Audio-Classification-Using-Wavelet-Transform

Classifying audio using Wavelet transform and deep learning

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LiDAR

LiDAR classification using neighbourhood region of NxN surrounding a pixel.

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Music-genre-classification-part-2

Music genre classification using an ensemble of different features

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SincNet

SincNet is a neural architecture for efficiently processing raw audio samples.

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ISPRS_S2FL

Danfeng Hong, JIngliang Hu, Jing Yao, Jocelyn Chanussot, Xiao Xiang Zhu. Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model, ISPRS JP&RS, 2021.

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LiDARTutorial

A Brief Tutorial on LiDAR data visualisation and classification

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JuliaDataAnalysis

The goal of this project is to use Julia to load data into an SQLite database, create a dataframe, and run different queries.

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SER-datasets

A collection of datasets for the purpose of emotion recognition/detection in speech.

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Awesome-VAEs

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

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Awesome-Knowledge-Distillation

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

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Bird-sound-classification

Classify bird's sound using siamese networks and few-shot learning.

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Image-segmentation-overview

Demonstration of a few useful segmentation algorithms.

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Gossip-Simulator

Implementation of gossip protocols for information dissemination in a network with different kinds of topologies.

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Bitcoin-Simulator

Implementation of Bitcoin protocol to simulate bitcoin mining, wallet, and transactions.

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MultiColor-Shapes-Database

A small database to test different machine learning tasks. It contains simple shapes of different colors.

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SIFT-Algorithm

Demonstration of sift algorithm to track objects and observing the effect of each parameter on performance.

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Linear-and-Circular-Convolution-using-FFT

This program demonstrates (i) the speedup obtained by using FFTs in numerical convolution. The two sequences x and y must contain at least 1000 elements each. The convolution code is written on own and libraries are used for the FFT computation. The speedup is documented using TIC TOC. (ii) The errors between circular convolution using FFTs and linear convolution (direct computation) is documented. In both (i) and (ii), 5 sets of random x and y sequences are used.

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SVD-based-image-reconstruction

Load the hendrix_final.png image and extract the R, G and B channels. Convert each channel image to double precision. Then execute the SVD separately on the R, G and B channels of the image. Plot (using a log-log plot) the non-zero singular values for the R channel. Comment on the nature of the plot. Plot the Frobenius norm of the reconstruction error matrix for each channel w.r.t. the dimension (increasing from 1 to the rank) and display the original and final reconstructed images (combined from R, G and B reconstructions)

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Music-Genre-Classification

Classify music in two categories progressive rock and non-progressive rock using mfcc features, MLP, and CNN.

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Tensorboard_visualize

Visualize data on TensorBoard.

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Bird-Song-Classification

Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.

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pauses

🎤 quick library to extract pause lengths from audio files.

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A-Guide-to-Wavenet

A Wavenet Primer. From audio preparation to audio generation.

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