Recognizing the genre of music files using machine learning and deep learning models
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Updated
May 23, 2021 - Jupyter Notebook
Recognizing the genre of music files using machine learning and deep learning models
Deep learning based content moderation from text, audio, video & image input modalities.
공개 한국어 감성 검색 엔진 - 2017 제 11회 공개SW개발자대회 결선작
🎵 Using Deep Learning to Categorize Music as Time Progresses Through Spectrogram Analysis
Genre Classification using Convolutional Neural Networks
Generate vector embeddings for music
🎵 Trained CNN model for Genre classification on GTZAN dataset [CNN Model: https://github.com/Hguimaraes/gtzan.keras]
Data pipeline and training pipeline for 🎵 music genre classification from FMA dataset
Application for processing news (and other) articles
This is Content based music recommendation system. I have used both audio features and lyrics (text based) features for recommending most similar songs to a given query song.
Genre Detection of Bengali Rabindranath Tagore's Song Based On Audio Data.
Genre Classification Model Based on VGGish
to classify music into different genres
The project consists in evaluating music similarity and building a genre classifier using song embeddings from GTZAN dataset extracted with Essentia’s MSD-MusiCNN model.
Categorize audio files by genre effortlessly. Use Dockerized environment and API to classify music genres.
Music similarity and feature extraction experiments
A training script for a bi-layer neural network to detect the genre of songs by converting them into spectrograms and using CNNs and RNNs to classify them. Trained using the FMA (full music archive) dataset.
A music genre classification project. Audio source: gtzanetakis, Million Song Dataset; ML Libraries: Keras, Tensorflow, Pytorch; NN Models: CNN, RNN.
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