228 skills found · Page 1 of 8
ForLoopCodes / ContextplusSemantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy. By combining RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-style linking, Context+ turns a massive codebase into a searchable, hierarchical feature graph.
kzampog / CilantroA lean C++ library for working with point cloud data
shobrook / CommunitiesLibrary of graph clustering algorithms
wq2012 / SpectralClusterPython re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.
derrickburns / Generalized Kmeans ClusteringProduction-ready K-Means clustering for Apache Spark with pluggable Bregman divergences (KL, Itakura-Saito, L1, etc). 6 algorithms, 740 tests, cross-version persistence. Drop-in replacement for MLlib with mathematically correct distance functions for probability distributions, spectral data, and count data.
KlugerLab / SpectralNetDeep network that performs spectral clustering
FilippoMB / Spectral Clustering With Graph Neural Networks For Graph PoolingReproduces the results of MinCutPool as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling".
gionanide / Speech Signal Processing And ClassificationFront-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].
Wordcab / Wordcab Transcribe💬 ASR FastAPI server using faster-whisper and Multi-Scale Auto-Tuning Spectral Clustering for diarization.
dimkastan / PyTorch Spectral Clustering[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering implemented with Pytorch
tango4j / Auto Tuning Spectral ClusteringThis repo is for the SPL paper "Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap"
kugelrund / Mesh SegmentationA python addon for mesh segmentation in blender using spectral clustering methods
youweiliang / Multi View ClusteringMATLAB code for 7 Multi-view Spectral Clustering algorithms
wlwkgus / DeepSpectralClusteringPytorch Implemention of paper "Deep Spectral Clustering Learning", the state of the art of the Deep Metric Learning Paper
pthimon / ClusteringLibrary for performing spectral clustering in C++
wanxueyao / MMGraphRAGMMGraphRAG is a multi-modal knowledge graph-based framework designed to enhance complex reasoning tasks, such as multi-modal document question-answering. It integrates text and image data into a fine-grained, structured knowledge graph, utilizing scene graphs for image data and a spectral clustering-based fusion module.
SongDark / SpectralClusteringPython implementation of Spectral Clustering.
noelshin / Selfmask[CVPRW'22] Unsupervised Salient Object Detection With Spectral Cluster Voting
pin3da / Spectral ClusteringPython implementation of the spectral clustering algorithm
xdxuyang / Deep Spectral Clustering Using Dual Autoencoder NetworkNo description available
