spectral-embedding

There are 18 repositories 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].

    Language:Python23710463

  • 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 Notebook2145637

  • baggepinnen/SpectralDistances.jl

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

    Language:Julia46454

  • JAVI897/Laplacian-Eigenmaps

    Implemented Laplacian Eigenmaps

    Language:Jupyter Notebook18134

  • gattia/pyfocusr

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  • PKU-ML/LaplacianCanonization

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

    Language:Python7301

  • PyDimRed/PyDimRed

    A comparison between some dimension reduction algorithms

    Language:Jupyter Notebook5302

  • dcellwanger/CellTrails

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

    Language:R3222

  • GeorgeMLP/laplacian-canonization

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

    Language:Python2200

  • Wangchenchen233/ITPC

    Knowledge-Based Systems, Wang, Chenchen, Zhichen Gu, and Jin-Mao Wei. "Spectral clustering and embedding with inter-class topology-preserving." 2024.

    Language:Python210

  • 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".

    Language:Python1100

  • 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 Notebook1100

  • 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

    Language:Jupyter Notebook120

  • Aganonce/py_spec_embed

    Python implementation of network spectral embedder.

    Language:Python0200

  • arjunsawhney1/face-ML

    In this repo, I demonstrate how simple Linear Algebra concepts can be utilized for powerful image element detection applications

    Language:Jupyter Notebook0100

  • hhuang5163/Book-Author-Clustering

    Clustering exploration using the authors dataset

    Language:Python0100

  • mohashei/Dimensional-Reduction

    Applying dimensional reduction techniques to Kepler data.

    Language:Jupyter Notebook0200

  • Sarvandani/Machine_learning_6_algorithms_of_dimensionality_reduction

    Sklearn, PCA, t-SNE, Isomap, NMF, Random Projection, Spectral Embedding

    Language:Jupyter Notebook10