122 skills found · Page 1 of 5
gionanide / Speech Signal Processing And ClassificationFront-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 employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
BehnoodRasti / HyFTech Hyperspectral Shallow Deep Feature Extraction ToolboxThis Toolbox includes Hyperspectral Feature Extraction Techniques including Unsupervised, Supervised, and Deep Feature Extraction
BarnesLab / Patient2VecPatient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
a-n-rose / Python Sound ToolSoundPy (alpha stage) is a research-based python package for speech and sound. Applications include deep-learning, filtering, speech-enhancement, audio augmentation, feature extraction and visualization, dataset and audio file conversion, and beyond.
ari-dasci / S TSFE DLTime Series Feature Extraction using Deep Learning
YOHNGGG / Deep Learning Based Spectrum SensingThe codes for the method that uses feature extraction and deep learning to do spectrum sensing for cognitive radio.
Erfaniaa / Persian OCROffline optical character recognition of Farsi and Arabic letters using deep learning and feature extraction (تشخیص حروف فارسی و عربی آفلاین با استفاده از یادگیری عمیق و استخراج ویژگیها)
abishek-as / Audio Classification Deep LearningWe'll look into audio categorization using deep learning principles like Artificial Neural Networks (ANN), 1D Convolutional Neural Networks (CNN1D), and CNN2D in this repository. We undertake some basic data preprocessing and feature extraction on audio sources before developing models. As a result, the accuracy, training time, and prediction time of each model are compared. This is explained by model deployment, which allows users to load the desired sound output for each model that is successfully deployed, as will be addressed in more depth later.
MrinmoiHossain / Udacity Deep Learning NanodegreeThe course is contained knowledge that are useful to work on deep learning as an engineer. Simple neural networks & training, CNN, Autoencoders and feature extraction, Transfer learning, RNN, LSTM, NLP, Data augmentation, GANs, Hyperparameter tuning, Model deployment and serving are included in the course.
banafshebamdad / SELM SLAM3A deep learning-powered RGB-D SLAM framework leveraging SuperPoint for feature extraction and LightGlue for feature matching, enhancing localization accuracy.
fuscc-deep-path / Sc MTOPsc-MTOP is an analysis framework based on deep learning and computational pathology. This framework aims to characterize the tumor ecosystem diversity at the single-cell level. This code provide 1) Hover-Net-based nuclear segmentation and classification; 2) Nuclear morphological and texture feature extraction; 3) Multi-level pairwise nuclear graph construction and spatial topological feature extraction.
Nagakiran1 / 4 Simple Steps In Builiding OCROptical character recognition (OCR) is process of classification of opti- cal patterns contained in a digital image. The character recognition is achieved through segmentation, feature extraction and classification. Keras Deep learning Network is used at here in recognising the Text characters and OpenCV is used in segmenting the text and Noise normalization.
scabini / RADAMWe propose a new method named Random encoding of Aggregated Deep Activation Maps (RADAM) for feature extraction from pre-trained Deep CNNs. The technique consists of encoding the output at different depths of the CNN using a Randomized Autoencoder, producing a single image descriptor
Baticsute / Facial Expression Recognition FER2013Images and Videos, Real-time Facial Expession Recognition Application with Combine CNN , deep learning features extraction incorporate SIFT, FAST feature .
mudigosa / Image ClassifierImage Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories. When you've completed this project, you'll have an application that can be trained on any set of labelled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. This is the final Project of the Udacity AI with Python Nanodegree Prerequisites The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install pip run in the command Line python -m ensurepip -- default-pip to upgrade it python -m pip install -- upgrade pip setuptools wheel to upgrade Python pip install python -- upgrade Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json. You can donwload them using pip pip install numpy pandas matplotlib pil or conda conda install numpy pandas matplotlib pil In order to intall Pytorch head over to the Pytorch site select your specs and follow the instructions given. Viewing the Jyputer Notebook In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using git clone https://github.com/fotisk07/Image-Classifier/ then in the command Line type, after you have downloaded jupyter notebook type jupyter notebook locate the notebook and run it. Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Json file In order for the network to print out the name of the flower a .json file is required. If you aren't familiar with json you can find information here. By using a .json file the data can be sorted into folders with numbers and those numbers will correspond to specific names specified in the .json file. Data and the json file The data used specifically for this assignemnt are a flower database are not provided in the repository as it's larger than what github allows. Nevertheless, feel free to create your own databases and train the model on them to use with your own projects. The structure of your data should be the following: The data need to comprised of 3 folders, test, train and validate. Generally the proportions should be 70% training 10% validate and 20% test. Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image a.jpj and it is a rose it could be in a path like this /test/5/a.jpg and json file would be like this {...5:"rose",...}. Make sure to include a lot of photos of your catagories (more than 10) with different angles and different lighting conditions in order for the network to generalize better. GPU As the network makes use of a sophisticated deep convolutional neural network the training process is impossible to be done by a common laptop. In order to train your models to your local machine you have three options Cuda -- If you have an NVIDIA GPU then you can install CUDA from here. With Cuda you will be able to train your model however the process will still be time consuming Cloud Services -- There are many paid cloud services that let you train your models like AWS or Google Cloud Coogle Colab -- Google Colab gives you free access to a tesla K80 GPU for 12 hours at a time. Once 12 hours have ellapsed you can just reload and continue! The only limitation is that you have to upload the data to Google Drive and if the dataset is massive you may run out of space. However, once a model is trained then a normal CPU can be used for the predict.py file and you will have an answer within some seconds. Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints: By increasing the number of epochs the accuracy of the network on the training set gets better and better however be careful because if you pick a large number of epochs the network won't generalize well, that is to say it will have high accuracy on the training image and low accuracy on the test images. Eg: training for 12 epochs training accuracy: 85% Test accuracy: 82%. Training for 30 epochs training accuracy 95% test accuracy 50%. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning process will take longer Densenet121 works best for images but the training process takes significantly longer than alexnet or vgg16 *My settings were lr=0.001, dropoup=0.5, epochs= 15 and my test accuracy was 86% with densenet121 as my feature extraction model. Pre-Trained Network The checkpoint.pth file contains the information of a network trained to recognise 102 different species of flowers. I has been trained with specific hyperparameters thus if you don't set them right the network will fail. In order to have a prediction for an image located in the path /path/to/image using my pretrained model you can simply type python predict.py /path/to/image checkpoint.pth Contributing Please read CONTRIBUTING.md for the process for submitting pull requests. Authors Shanmukha Mudigonda - Initial work Udacity - Final Project of the AI with Python Nanodegree
cedrusx / Deep FeaturesA ROS package for deep CNN-based feature extraction and publishing
singlasahil14 / BarlowCode for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction
swalpa / G2C Conv3D HSIPyTorch implementation of the paper - Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution
dakshayh / Face Spoofing DetectionDeep Texture feature extraction and implementing Local Binary Pattern(LBP)-based Convolutional Neural Network
adiengineer / ADHD Classification DBN ExtractionGuide : Prof Sundaram Suresh (NTU- Singapore) Area: Deep learning neural networks for feature extraction in high dimensional neuro imaging data. Tools used: Standard neuro imaging software for preprocessing, a MATLAB deep learning toolbox DeeBNet. I used deep learning algorithms including RBM’s and CNN’s to train on an open source MRI data set and classify unseen fMRI scans as having ADHD or not. I was able to achieve accuracy scores of 64% which is incrementally better than the current start of art(as of 2016). The project was challenging due to the high dimensionality of the input data and the meager number of test samples.
