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superhgq1's Projects

audio-classifier icon audio-classifier

music/speech classification by using discrete fourier transform and feeding the cnn a graphical visualization of sound

deeplog icon 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 icon failurepredict

A short collection of tutorial network exploring some failure prediction techniques.

gccestimating icon gccestimating

Generalized Cross Correlation Estimator implementation based on numpy.

real-time-vad icon real-time-vad

Synthesis of "A SIMPLE BUT EFFICIENT REAL-TIME VOICE ACTIVITY DETECTION ALGORITHM (2009)

speech-enhancement icon speech-enhancement

Collection of papers, datasets and tools on the topic of Speech Dereverberation and Speech Enhancement

thinkdsp icon thinkdsp

Think DSP: Digital Signal Processing in Python, by Allen B. Downey.

vad icon vad

Voice Activity Detection system (Matlab-based implementation)

vad-1 icon vad-1

Voice Activity Detection System

vad-2 icon vad-2

Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.

voice-activity-detector-algorithms icon 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"

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