Detecting emotions from audios using neural networks
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Updated
Nov 3, 2020
Detecting emotions from audios using neural networks
Verification using voice biometrics
ENCM 509 - Fundamentals of Biometric Systems Design - Winter 2024
Our goal is to push the general performance of music genre recognition forward and introduce a new method for pre-processing which allows for faster experimentation and model tuning in the future. We experimented with two different musical representations: mel-spectrograms and manually extracted features.
The voice-controlled robot system features four simple commands: forward, backward, left, and right.
Speaker Recognition deep learning model based on feature extraction from Mel Frequency Cepstral Coefficients
Voice Id Door Lock Web-App is a Speaker-Identification and Sentence-Verification using Voice MFCCs Feature and GMM
EE 338 (Digital Signal Processing) - Application Assignment
This project focuses on classifying Classical Music into its sub-genres. We have performed Data cleaning, Normalization and Standardization. We have extracted Mel-Frequency Cepstral Components(MFCC), dynamic, rhythm, tonal, and spectral features from the audio files. Algorithms like K-Nearest Neighbor, Random Forest, Support Vector Machine, Mult…
Machine Learning based Cough-Detection from Audiorecordings by using MFCCs, PCA and One-Class-SVM
Speaker verification system using MFCC’s features and MLP classifier.
A simple AI drawing interface controlled by voice commands
Reverse engineering sound.
multiple projects in speech processing
Audio classification model. PyTorch.
NLP Project for CS6120 at Northeastern University
In this project we have created a Artificial Neural Network to classify the audios along with Exploratory Data Analysis and Data Preprocessing.
A python wrapper for speech feature extractoin
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