Pinned Repositories
audio-classifier
music/speech classification by using discrete fourier transform and feeding the cnn a graphical visualization of sound
BeamForm
beamforming
Matlab files for various types of beamforming
beamforming-1
BeamformIt
BeamformIt acoustic beamforming software
DeepLog
Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution.
FailurePredict
A short collection of tutorial network exploring some failure prediction techniques.
gccestimating
Generalized Cross Correlation Estimator implementation based on numpy.
houguang
Network-Fault-Analysis
A project to build a predictive algorithm to preemptively catch lapses in a network
superhgq1's Repositories
superhgq1/gccestimating
Generalized Cross Correlation Estimator implementation based on numpy.
superhgq1/Network-Fault-Analysis
A project to build a predictive algorithm to preemptively catch lapses in a network
superhgq1/FailurePredict
A short collection of tutorial network exploring some failure prediction techniques.
superhgq1/DeepLog
Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution.
superhgq1/Network-Software-Fault-Prediction-Model
superhgq1/Thinking-in-AV
音视频开发知识库
superhgq1/BeamForm
superhgq1/projectRUL2
superhgq1/speech-enhancement
Collection of papers, datasets and tools on the topic of Speech Dereverberation and Speech Enhancement
superhgq1/BeamformIt
BeamformIt acoustic beamforming software
superhgq1/ThinkDSP
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
superhgq1/beamforming
Matlab files for various types of beamforming
superhgq1/screen-capture-recorder-to-video-windows-free
a free open source windows "screen capture" device and recorder (also allows VLC/ffmpeg and others to capture/stream desktop/audio)
superhgq1/Sound_Localization_Algorithms
Classical algorithms of sound source localization with beamforming, TDOA and high-resolution spectral estimation.
superhgq1/houguang
superhgq1/particleFilter
Particle filter for PHM
superhgq1/Network-Fault-Mangement
compartion of machine Learning tecniques for Network Fault Prediction
superhgq1/Temperature-prediction-using-Machine-Learning
Predicting the temperature of your system based on factors such as RAM usage,CPU storage temperature,Memory Used and space consumed by the applications( CPU load).
superhgq1/VAD-2
Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.
superhgq1/real-time-VAD
Synthesis of "A SIMPLE BUT EFFICIENT REAL-TIME VOICE ACTIVITY DETECTION ALGORITHM (2009)
superhgq1/audio-classifier
music/speech classification by using discrete fourier transform and feeding the cnn a graphical visualization of sound
superhgq1/Voice-Activity-Detector-Algorithms
This repository is based on the Voice Acitivyt Detectors (VAD) implemented on "Analysis of the use of noise removal techniques as preprocessing of speech activity detection algorithms"
superhgq1/vad
Voice Activity Detection system (Matlab-based implementation)
superhgq1/beamforming-1
superhgq1/VAD-1
Voice Activity Detection System