EE 338 (Digital Signal Processing) - Application Assignment

Multi-class audio classification with MFCC features using CNN

Java Implementation of the Sonopy Audio Feature Extraction Library by MycroftAI

Detecting emotion from audio data with MFCC features,spectogram and amplitude images by using CNN, LSTM and Machine Learning techniques

Audio classification using a simple SVM classifier making use of MFCC and Spectrogram features coded from scratch

A RESTFUL API implementation of an authentification system using voice fingerprint

NLP Project for CS6120 at Northeastern University

A Python implementation of STFT and MFCC audio features from scratch

Genre Detection of Bengali Rabindranath Tagore's Song Based On Audio Data.

Common-lisp implementation of MFCC

Training a model using CNN's to predict the emotion class of an Audio file in pytorch framework.

Classify music in two categories progressive rock and non-progressive rock using mfcc features, MLP, and CNN.

Reverse engineering sound.

MFCC features + SVM for speech emotion classification

Detecting emotions from audios using neural networks

Extracted ComParE features using OpenSMILE tool and MFCC features from scratch from the pathological dataset 2 and performed classification using both features and compared them.

Development of a Voice Activity Detector and a Speaker Recognition System. Feature extraction in time and frequency domain. Classification in ten individual speakers.

GTZAN Music genre classification using Logistic regression and SVM.