Identify the emotion of multiple speakers in an Audio Segment

Use machine learning models to detect lies based solely on acoustic speech information

A RESTFUL API implementation of an authentification system using voice fingerprint

Developed and trained Gated-CNN models to detect types of stutter in speech and SVM classifier to suggest new therapies to the user according to his stutter type and severity

Machine Learning Approach to built a robust speaker recognition model using MFCC features and GMM universal background model.

Implementation of Mel-Frequency Cepstral Coefficients (MFCC) extraction

The goal is to recognize and understand different patterns and features which make up the author’s unique style of writing and eventually predict who might have written a piece of work.

AI TECH 2021

This project focuses on real-time Speech Emotion Recognition (SER) using the "ravdess-emotional-speech-audio" dataset. Leveraging essential libraries and Long Short-Term Memory (LSTM) networks, it processes diverse emotional states expressed in 1440 audio files. Professional actors ensure controlled representation, with 24 actors contributing

Emotion Recognition using matlab (Machine Learning using SVM and Random Forest)

Tackle accent classification and conversion using audio data, leveraging MFCCs and spectrograms. Models differentiate accents and convert audio between accents

We use MFCC to convert heart sounds to images and to recognize images using the latest Google’s research called Vision Transformer(ViT).

Basic speech processing implementations

🎙Audio analysis - a field that includes automatic speech recognition(ASR)🎛, digital signal processing🎚, and music classification🎶, tagging📻, and generation🎧 - is a 🎼growing subdomain of 🎵deep learning applications🎤

SVM model using i-vector

MATLAB code for audio signal processing, emphasizing Real Cepstrum and MFCC feature extraction. Reads a wave file, applies Hamming and Rectangular windows, then computes Real Cepstrum. Utilizes MATLAB's built-in functions for extracting MFCC features. Perfect for audio analysis and feature engineering.

A comparison of two implementations of MFCC for audio preprocessing. Tested on Raspberry4.

Codes for Audio Representation Learning (EE798P) offered at IIT Kanpur and picked up by me in my seventh semester

In this project we have created a Artificial Neural Network to classify the audios along with Exploratory Data Analysis and Data Preprocessing.