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Augmented Pre-training Networks for Multi-type Signal Detection and Classification in Time-Frequency Spectrogram
A few-shot learning method for specific emitter identification or radio frequency fingerprintinig
Codes and template data for paper "Experiments with mmWave Automotive Radar Test-bed"
Self-Supervised Speech Pre-training and Representation Learning Toolkit
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.
(IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"
Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Feature extraction from speech signals based on representation learning strategies using pre-trained autoencoders
Audio Signal Preocessing: pcm2wav, wav2pcm, feature extraction, augment, delete silence etc
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem em…
Core package to analyze gravitational-wave data, find signals, and study their parameters. This package was used in the first direct detection of gravitational waves (GW150914), and is used in the …
A curated list of data mining papers about fraud detection.
pyclustering is a Python, C++ data mining library.
Machine learning, statistics, and data mining for astronomy and astrophysics
Designed a real-time audio mining system to extract MFCC features from voice signals for gender detection, speaker recognition and emotion analysis using a GMM and SVM
Comprehensive Matlab framework for signal, audio and music analysis, articulating audio and symbolic approaches
Implementation of Differentiable Digital Signal Processing (DDSP) in Pytorch
Digital signal processing for neural time series.
Data manipulation and transformation for audio signal processing, powered by PyTorch
CubeSLAM: Monocular 3D Object Detection and SLAM
Real-time 3D Scene Layout from a Single Image Using Convolutional Neural Networks
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Semantic 3D Occupancy Mapping through Efficient High Order CRFs
The mutli-task work of <Monocular Outdoor Semantic Mapping with a Multi-task Network>