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Hadryan / Music_Genre_Classification
Machine Learning Algorithms for Music Genre Classification
Language:Python 3DRON12261 / DronsRayTracer-SceneEditor
[РЕДАКТОР СЦЕН] Разработка приложения для построения 3D моделей с помощью трассировки лучей
Language:GDScript 1Music genre classification with LSTM Recurrent Neural Nets in Keras & PyTorch
Language:Python 1Recognizing the genre of music files using machine learning and deep learning models
Language:Jupyter Notebook 1Music is a medium to express emotion. According to literature, music emotion can be quantified continuously as valence and arousal (VA) density distribution on a 2-D space. However, these data are hard to retrieve as they require intense human effort to manually label songs, especially the number of songs become enormous. The goal of this project is to reproduce a model proposed by Chin, Y.-H. et.al (2018), to predict VA density for a new song based on those densities for training songs as well as audio features of both new and training songs. This will help save human labeling effort on new songs in the future. Furthermore, a prototype of content-based music recommender system is built to demonstrate the usability of the algorithm.
Language:Jupyter NotebookExploring Audio Features and building a Machine Learning Approach
Language:Jupyter NotebookA multimodal approach on emotion recognition using audio and text.
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Multi class audio classification with MFCC features using CNN
Language:Jupyter NotebookToolkit for downloading and processing Google's AudioSet dataset.
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[in progress] Music Recommendation System using Million Song Dataset along with Spotify audio features (Good)
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Project centered around the Spotify API that uses a song's audio features to use exploratory data analysis, along with various classification ML algorithms to predict a user's liked songs
Language:PythonAccurately predicting the genre of a song based on its audio features.
Language:Jupyter NotebookEmotion detection in audio utilising self-supervised representations trained with Contrastive Predictive Coding (CPC).
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The code for the ISMIR 2019 paper “Supervised symbolic music style translation using synthetic data”.
Language:Pythontakes isrc and returns audio features as determined by spotify
Language:Pythonhttps://www.kaggle.com/c/kkbox-music-recommendation-challenge
Language:PythonUsing Genius annotations and Spotify audio features to recommend songs similar in topic and musicality
Language:Jupyter NotebookOur task is to Recognize whether an image of a hand-written digit and a recording of a spoken digit refer to the same or different number. We have two input data written number image and number spoken sound MFCC features and one output consist of boolean array state that the respective sound and image matches or not. We choose multi model approach using LSTM for audio features and CNN for image data. The output of both model concatenated at the end and binary loss function applied.
Language:Jupyter NotebookAutomatic Music Genre Classification with Machine Learning Techniques
Language:Jupyter NotebookUsing deep learning to predict the genre of a song.
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Musical genre classification capstone (Good)
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crawling and web api usage practice with book, music, and cafe subjects
Language:PythonCaffe Squeezenet model for binary classification of pornographic/non-pornographic material
Library for free use Google Translator. With attempts connecting on failure and array support.
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Use supervised classification methods to predict song success using Spotify's audio feature
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