Tooling and datasets for neural-network powered audio feature based synthesis

TuneSpy is a Python application that allows users to load audio files, generate spectrograms, extract MFCC features, and compare the loaded audio with a preprocessed database of songs to find the most similar match.

AudioInspect is an app that extracts audio features from uploaded audio files or audio files in a specified folder, providing insights into the characteristics of the audio.

A simple music feature extractor for Deep Learning models

Developed a deep learning model using Multi-Layer Perceptron to recognize and classify speech signals into 6 distinct emotions. Extracted 160 audio features, enabling the model to detect emotions with around 75% accuracy on the training set. Implemented the model on a Streamlit dashboard.

Python Script to suggest the volume at which the music audio file needs to be played for better experience and feeling.

Created as part of Audio and Music processing lab assignment. Extracts and analyses features from an audio collection, and creates playlists based on various descriptors. Can create playlists based on music similarity too.

Fingerprint-based music and vocals identification app that generates spectrograms, extracts features, applies perceptual hashing, and finds the most similar songs based on fingerprint matching.

An intelligent music mood analyzer that decodes the emotional essence of songs through advanced machine learning. By analyzing six key audio features, this system achieves remarkable 95%+ accuracy in classifying music into four distinct emotional categories. Perfect for creating emotionally intelligent playlists, music recommendation systems.