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audio-categorization-using-machine-learning

Audio Categorization

audio-classification

:musical_score: Environmental sound classification using Deep Learning with extracted features

audio-classification-features

Complete Package for Audio Classification

audio-classifier

CNN based audio classifier by pytorch (LeNet / VGG / ResNet)

audio-digits-classification-using-mfcc-and-convolutional-neural-network

This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program Module named “Machine Learning” in Queen Mary University of London (QMUL), London, United Kingdom. The project cover the basic solution and the Advanced Solution as given below based on Audio Feature Extraction Method named "Mel-frequency cepstral coefficients (MFCC)" and Deep Learning Convolutional Neural Network (CNN). Basic Solution: Includes designing, building, training, validation and testing a model created to recognise numerals from 0 to 9 in the audio files. Advanced Solution: Includes implementing the solution to predict the numeral based on a new audio test file. This model's solution can be applied to a Banking Application/Product and can be used for predicting a 4-digit passcode said by an authorised customer during on-call verification as part of login process to Internet Banking Account. NOTE: Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.

audio-emotion-recognition

CZ4042 Group project: audio emotion recognition

audio-emotion-recognition-1

Audio Emotion Recognition using machine learning algorithms

audio-feature-analysis

Tool that uses the Spotipy library to scrape audio features from the first 100 songs in a given Spotify playlist. Outputs a mean and standard deviation for each feature

audio-feature-engineering

A basic understanding of Audio Features

audio-feature-extraction

audio-feature-extraction-1

audio-feature-extraction-2

Perform emotion recognition from speech using deep learning strategy

audio-feature-extraction-3

In sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC.

audio-feature-extractions

https://github.com/aith/audio-feature-extractions/blob/main/index.ipynb

audio-feature-recommender-project

Recommends music based on audio features using Spotify

audio-feature-visualisation

Visualisation of audio feature using Spotify API. Built with node + vue + chartjs

audio-features

An attempt to standardize all audio features `python` library.

audio-features-1

Do feature visualization on audio neural networks.

audio-features-artist-popularity-influence-on-song-popularity

Final project for COGS 108 at UCSD.

audio-features-for-music-performance-analysis

A simple python module for extracting a set of audio features useful for music performance analysis

audio-features-unsupervised-learning

In this project I clustered my Spotify music using its audio features with the goal of finding similar music in each of the clusters

audio-features-visualizer

audio-fingerprint

Based on "Wang, A. (2003, October). An industrial strength audio search algorithm. In Ismir (Vol. 2003, pp. 7-13)."

audio-genre-classification

Automatic music genre classification using Machine Learning algorithms like- Logistic Regression and K-Nearest Neighbours

audio-music-mood-classification

Classification using audio features to generate mood profiles for spotify playlists

audio-player

A complete mp3 player with all features using API calls for fetching songs

audio-processing-

I worked with audio processing using Librosa. Using Mel-Power, Chroma Features, Perceptual Weighting and Log Graphing.

audio-recommender

Draft audio recommender system written with Python (pandas + Flask)

audio-soup

Sample Review & Feature Selection for Audio Datasets

audio-source-separation

WaveNet for the separation of audio sources