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
A corpus that can be used to train English-to-Italian End-to-End Speech-to-Text Machine Translation models
Program to detect sentiment from audio files.
Identify speaker from given speech signal using MFCC features and Gaussian Mixture Models
GTZAN Music genre classification using Logistic regression and SVM.
Fluency level classifier of L2 English speech
Common-lisp implementation of MFCC
Training a model using CNN's to predict the emotion class of an Audio file in pytorch framework.
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.
Implementation of Mel-Frequency Cepstral Coefficients (MFCC) extraction
Tackle accent classification and conversion using audio data, leveraging MFCCs and spectrograms. Models differentiate accents and convert audio between accents
A machine learning model is trained to determine the word in an audio file
SVM model using i-vector
Voice Id Door Lock Web-App is a Speaker-Identification and Sentence-Verification using Voice MFCCs Feature and GMM
Hate speech detection in audio for English and Kiswahili languages
Application of a convolutional neural network (CNN) to accurately classify urban sounds in a bid to increase pedestrian safety using the UrbanSound8k dataset.
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