230 skills found · Page 1 of 8
danfenghong / IEEE TPAMI SpectralGPTHong, D., Zhang, B., Li, X., Li, Y., Li, C., Yao, J., Yokoya, N., Li, H., Ghamisi, P., Jia, X., Plaza, A. and Gamba, P., Benediktsson, J., Chanussot, J. (2024). SpectralGPT: Spectral remote sensing foundation model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. DOI:10.1109/TPAMI.2024.3362475.
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].
emadeldeen24 / TSLANet[ICML 2024] A novel, efficient lightweight approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks
Luckick / EAGCNMulti-View Spectral Graph Convolution with Consistent Edge Attention for Molecular Modeling
FuSiry / OpenSAAiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process, a complete algorithm library is established, which is named opensa (openspectrum analysis).
dario-passos / DeepLearning For VIS NIR SpectraDeep Learning models applied to the analysis of VIS-NIR spectral data
FurongHuang / Spectrallda TensorsparkQuick summary: This code implements a spectral (third order tensor decomposition) learning method for learning LDA topic model on Spark.
yerongke / Hyperspectral Data ProcessingSpectral data processing, including preprocessing, feature extraction, sample division, modeling, optimization algorithm.
thuml / Latent Spectral ModelsAbout Code Release for "Solving High-Dimensional PDEs with Latent Spectral Models" (ICML 2023), https://arxiv.org/abs/2301.12664
davidhowey / Spectral Li Ion SPMSpectral li-ion SPM is a MATLAB code that solves the so-called lithium-ion battery Single Particle Model (SPM) using spectral numerical methods.
PURE-melo / S2MambaThe official code for the paper "S2Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification" The code will be available soon.
lronkitty / SSUMambaCode of "SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising"
threeML / AstromodelsSpatial and spectral models for astrophysics
fastlmm / FaST LMMPython version of Factored Spectrally Transformed Linear Mixed Models
IBM / Remote Sensing Image RetrievalMulti-Spectral Remote Sensing Image Retrieval using Geospatial Foundation Models
Dogiye12 / Illegal Logging Detection Via Satellite And MLThis project demonstrates how synthetic satellite imagery data can be used to train a machine learning model for detecting illegal logging activities. Using simulated spectral bands (Red, NIR, SWIR) and derived vegetation indices (NDVI) along with texture features, the project classifies areas as either logged or non-logged.
Okes2024 / Modelling Urban Heat Islands From Satellite ImagerySynthetic modeling of Urban Heat Islands (UHI) using satellite-like data. Generates spectral bands, vegetation and urban indices, and land surface temperature (LST) for testing machine learning models, validating geospatial workflows, and exploring UHI dynamics without requiring real satellite imagery.
dronemapper-io / CropAnalysisA jupyter notebook with crop analysis algorithms utilizing digital elevation models and multi-spectral imagery (R-G-B-NIR-Rededge-Thermal)
CXH-Research / IRFormer[IJCNN 2024] Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Model
mims-harvard / SPECTRASPECTRA: Spectral framework for evaluation of biomedical AI models
