spectral-clustering

A lean C++ library for working with point cloud data

Library of graph clustering algorithms

Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.

Reproduces the results of MinCutPool as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling".

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].

implement the machine learning algorithms by python for studying

Spectral clustering algorithms written in Julia

Code for the CVPR 2019 paper : Spectral Metric for Dataset Complexity Assessment

Community Detection in Graphs (master's degree short project)

Implementation of "Just Balance GNN" for graph classification and node clustering from the paper "Simplifying Clusterings with Graph Neural Networks".

Moving Object Detection for Event-based vision using Graph Spectral Clustering (Python implementation)

CoRelAy is a tool to compose small-scale (single-machine) analysis pipelines.

Graph Agglomerative Clustering (GAC) toolbox

Robust Spectral Clustering. Implementation of "Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings".

A simple implementation of our paper

[WACV 2023] A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation

Graph Agglomerative Clustering Library

TKDE 2020: Ultra-Scalable Spectral Clustering and Ensemble Clustering (U-SPEC & U-SENC) #large-scale spectral clustering# #large-scale ensemble clustering#

MATLAB code for the ICDM paper "Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering"

Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.