spectral-embedding

There are 1 repository under spectral-embedding topic.

gionanide / Speech_Signal_Processing_and_Classification
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 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].
speech-processing mfcc linear-prediction-coefficients classifier speech-utterance feature-extraction support-vector-machines gaussian-mixture-models long-short-term-memory principal-component-analysis kernel-pca linear-discriminant-analysis isomap locally-linear-embedding spectral-embedding spectral-clustering natural-language-processing nlp nltk
Language:Python 230
drewwilimitis / Manifold-Learning
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
python manifold-learning dimensionality-reduction isomap multidimensional-scaling locally-linear-embedding spectral-embedding laplacian-eigenmaps
Language:Jupyter Notebook 201
baggepinnen / SpectralDistances.jl
Measure the distance between two spectra/signals using optimal transport and related metrics
spectrum spectral-embedding spectral-analysis linear-systems wasserstein-distance optimal-transport dynamic-frequency-warping riemannian-manifold metrics histogram earth-movers-distance barycenter barycentric-coordinates dynamic-time-warping
Language:Julia 45
JAVI897 / Laplacian-Eigenmaps
Implemented Laplacian Eigenmaps
dimensionality-reduction manifold-learning spectral-embedding laplacian-eigenmaps
Language:Jupyter Notebook 18
gattia / pyfocusr
point-cloud pointcloud registration graph graph-theory spectral spectral-embedding laplacian
Language:Python 5
PKU-ML / LaplacianCanonization
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
gnn laplacian-eigenmaps positional-encoding spectral-embedding
Language:Python 5
PyDimRed / PyDimRed
A comparison between some dimension reduction algorithms
dimension-reduction pca isomap spectral-embedding random-projection dimensionality-reduction laplacian-eigenmaps
Language:Jupyter Notebook 5
dcellwanger / CellTrails
Mirror of the Bioconductor package CellTrails (http://bioconductor.org/packages/CellTrails/)
clustering data-visualization dimensionality-reduction gene-expression-dynamics single-cell spectral-embedding trajectory-inference
Language:R 3
GeorgeMLP / basis-invariance-synthetic-experiment
Basis invariance synthetic experiment in Appendix D of NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
gnn laplacian-eigenmaps positional-encoding spectral-embedding
Language:Python 1
GeorgeMLP / laplacian-canonization
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
gnn laplacian-eigenmaps positional-encoding spectral-embedding
Language:Python 1
jgurakuqi / graph-kernels-and-manifold-svm
This project aims to compare the performance obtained using a linear Support Vector Machine model whose data was first processed through a Shortest Path kernel with the same SVM, this time with data also processed by two alternative Manifold Learning techniques: Isomap and Spectral Embedding.
graph-kernels isomap laplacian-eigenmaps manifold-learning shortest-path-algorithm sklearn spectral-embedding svm artificial-intelligence machine-learning multiprocessing
Language:Jupyter Notebook 1
yousefkotp / Network-Anomaly-Detection
This project focuses on network anomaly detection due to the exponential growth of network traffic and the rise of various anomalies such as cyber attacks, network failures, and hardware malfunctions. This project implement clustering algorithms from scratch, including K-means, Spectral Clustering, Hierarchical Clustering, and DBSCAN
anomaly anomaly-clustering anomaly-detection anomaly-detection-algorithm clustering clustering-algorithm db-scan dbscan dbscan-clustering dendogram density-based-clustering hierarchical-clustering k-means k-means-clustering k-means-implementation-in-python spectral-clustering spectral-embedding unsupervised-clustering unsupervised-machine-learning network-anomaly
Language:Jupyter Notebook 1
Aganonce / py_spec_embed
Python implementation of network spectral embedder.
clustering network-analysis python spectral-embedding
Language:Python 0
arjunsawhney1 / face-ML
In this repo, I demonstrate how simple Linear Algebra concepts can be utilized for powerful image element detection applications
eigenvectors image-classification image-recognition laplacian-eigenmaps linear-algebra pca spectral-embedding
Language:Jupyter Notebook 0
hhuang5163 / Book-Author-Clustering
Clustering exploration using the authors dataset
biclustering clustering mds nmf pca-analysis spectral-embedding tsne unsupervised-machine-learning
Language:Python 0
mohashei / Dimensional-Reduction
Applying dimensional reduction techniques to Kepler data.
tsne spectral-embedding
Language:Jupyter Notebook
Sarvandani / Machine_learning_6_algorithms_of_dimensionality_reduction
Sklearn, PCA, t-SNE, Isomap, NMF, Random Projection, Spectral Embedding
dimensionality-reduction dimensionality-reduction-algorithms isomap machine-learning nmf pca random-projection sklearn spectral-embedding t-sne
Language:Jupyter Notebook

spectral-embedding

gionanide / Speech_Signal_Processing_and_Classification

drewwilimitis / Manifold-Learning

baggepinnen / SpectralDistances.jl

JAVI897 / Laplacian-Eigenmaps

gattia / pyfocusr

PKU-ML / LaplacianCanonization

PyDimRed / PyDimRed

dcellwanger / CellTrails

GeorgeMLP / basis-invariance-synthetic-experiment

GeorgeMLP / laplacian-canonization

jgurakuqi / graph-kernels-and-manifold-svm

yousefkotp / Network-Anomaly-Detection

Aganonce / py_spec_embed

arjunsawhney1 / face-ML

hhuang5163 / Book-Author-Clustering

mohashei / Dimensional-Reduction

Sarvandani / Machine_learning_6_algorithms_of_dimensionality_reduction