spectral-embedding | Topic | Ecosyste.ms: Repos

Topic: "spectral-embedding"

drewwilimitis/Manifold-Learning

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

Language: Jupyter Notebook - Size: 140 MB - Last synced at: 17 days ago - Pushed at: about 5 years ago - Stars: 232 - Forks: 39

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].

Language: Python - Size: 827 KB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 220 - Forks: 62

baggepinnen/SpectralDistances.jl

Measure the distance between two spectra/signals using optimal transport and related metrics

Language: Julia - Size: 229 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 46 - Forks: 4

JAVI897/Laplacian-Eigenmaps

Implemented Laplacian Eigenmaps

Language: Jupyter Notebook - Size: 2.5 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 15 - Forks: 5

gattia/pyfocusr

Language: Python - Size: 12.8 MB - Last synced at: 29 days ago - Pushed at: about 1 year ago - Stars: 8 - Forks: 0

PKU-ML/LaplacianCanonization

Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".

Language: Python - Size: 88.9 KB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 5 - Forks: 1

dcellwanger/CellTrails

Mirror of the Bioconductor package CellTrails (http://bioconductor.org/packages/CellTrails/)

Language: R - Size: 41.8 MB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 3 - Forks: 2

PyDimRed/PyDimRed

A comparison between some dimension reduction algorithms

Language: Jupyter Notebook - Size: 25.8 MB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 3 - Forks: 2

GeorgeMLP/laplacian-canonization

Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".

Language: Python - Size: 88.9 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

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.

Language: Jupyter Notebook - Size: 70.3 MB - Last synced at: about 2 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0