locally-linear-embedding
There are 0 repository under locally-linear-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-processingmfcclinear-prediction-coefficientsclassifierspeech-utterancefeature-extractionsupport-vector-machinesgaussian-mixture-modelslong-short-term-memoryprincipal-component-analysiskernel-pcalinear-discriminant-analysisisomaplocally-linear-embeddingspectral-embeddingspectral-clusteringnatural-language-processingnlpnltkLanguage:Python 252drewwilimitis / 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 245bghojogh / Generative-LLE
The code for Generative Locally Linear Embedding (GLLE).
Language:Python 14OguzhanUlucan / Image-Fusion-Through-Linear-Embeddings-IEEE-ICIP-21
Official code of Image Fusion Through Linear Embeddings (IEEE ICIP 21)
Language:MATLAB 4The code for Locally Linear Image Structural Embedding (LLISE) and Kernel LLISE
Language:Python 2mpolinowski / manifold-learning-for-image-segmentation
Use Manifold Learning, Mapping and Discriminant Analysis to Visualize Image Datasets
Language:Jupyter Notebook 1mohammad95labbaf / UMAP_breast_cancer
This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.
Language:Jupyter Notebook 0Official Implementation of Multi-Exposure Image Fusion based on Linear Embeddings and Watershed Masking
Language:MATLAB 0Improve Data Quality by discarding non-correlating, noisy Dimensions
Language:Jupyter Notebooksayarghoshroy / Manifold_Learning
Concepts in Manifold Learning and Spectral Clustering Techniques
Language:Jupyter Notebook