Popular GitHub Repositories for Cov Spectrum

Benchmarking the Spectrum of Agent Capabilities

Anomaly detection library based on singular spectrum transformation(sst)

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)

Browse Covid-19 & SARS-CoV-2 Scientific Papers with Transformers ? ?

Singular Spectrum Analysis for time series forecasting in Python

Spectrum simulation attack (ECCV'2022 Oral) towards boosting the transferability of adversarial examples

Detecting Autism Spectrum Disorder in Children With Computer?Vision - Adapting facial recognition models to detect Autism Spectrum?Disorder

Full Spectrum AI

一个 Python 库，用于计算两个输入之间的梅尔倒谱距离（梅尔倒谱失真，MCD）。此实现基于 Robert F. Kubichek 在“用于客观语音质量评估的梅尔倒谱距离度量”中提出的方法。

Code for processing and managing data for EEG-based emotion recognition of individuals with and without Autism. EEG and other clinical data were collected in StonyBrook Social Competence Treatment Lab, for data request evaluation please contact professor Matthew D. Lerner matthew.lerner@stonybrook.edu

一个用于计算两个输入之间梅尔倒谱距离 (Mel-Cepstral Distortion, MCD) 的 Python 库。此实现基于 Robert F. Kubichek 在“用于客观语音质量评估的梅尔倒谱距离度量”中提出的方法。

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, its shows a four stacked LSTM network for early predi

[CVPR 2023] Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark

Launched in March 2020 in response to the coronavirus disease 2019 (COVID-19) pandemic, COVID-Net is a global open source, open access initiative dedicated to accelerating advancement in machine learning to aid front-line healthcare workers and clinical institutions around the world fighting the continuing pandemic. Towards this goal, our global multi-disciplinary team of researchers, developers, and clinicians have made publicly available a suite of tailored deep neural network models for tackling different challenges ranging from screening to risk stratification to treatment planning for patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, we have made available fully curated, open access benchmark datasets comprised of some of the largest, most diverse patient cohorts from around the world.

[WWW 2023] "Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

Deep Convolutional Neural Networks for Raman Spectrum Recognition. (RRUFF dataset)

speech enhancement GAN on waveform/log-power-spectrum data using Improved WGAN

[CVPR 2021: Oral] In this work, we show that high frequency Fourier spectrum decay discrepancies are not inherent characteristics for existing CNN-based generative models.

[IEEE TMI-2024] SASAN: Spectrum-Axial Spatial Approach Networks for Medical Image Segmentation

Official repository for "GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer" (ICASSP 2023)