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-ensemble-of-deep-learning-visual-and-acoustic-features-for-music-genre-classification

Ensemble of deep learning, visual and acoustic features for music genre classification

-music-recommendation-based-on-face-emotion-recognition

Facial expressions are captured using an inbuilt camera. Feature extraction is performed on input face images to detect emotions such as happy, angry, sad, surprise, and neutral. Automatically music playlist is generated by identifying the current emotion of the user. Convolutional Neural Network is used for emotion detection. For music recommendations, Pygame & Tkinter are used.

-musicrecommendationusingfer

00chart

100-days-of-ml-code

100 Days of ML Coding

100daysofmlcode

This is a pledge to dedicate 1 hour to Machine Learning everyday

1on1-questions

Mega list of 1 on 1 meeting questions compiled from a variety to sources

2019-emotion-and-theme-recognition-in-music-task

The goal of this task is to automatically recognize the emotions and themes conveyed in a music recording using machine learning algorithms.

201912-20-music-emotion-recognition

Music 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.

2020-flight-booking-system

2020_recsys_musicrecommender

Music Recommendation Implementation

2022-traffic-accident-prediction

2022-user-behavior-prediction

2022-wechat-big-data-challenge

30-seconds-of-interviews

A curated collection of common interview questions to help you prepare for your next interview.

31-days-of-api-security-tips

This challenge is Inon Shkedy's 31 days API Security Tips.

33-js-concepts

📜 33 concepts every JavaScript developer should know.

3d-owis

3D-OWIS is capable of detecting unknown instances in inference, and progressively learning novel classes in the process of training.

3d-photo-inpainting

[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting

3dgpt

3difftection

3dtagcloudandroid

Tagcloud component for android

4d-humans

4DHumans: Reconstructing and Tracking Humans with Transformers

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

60_days_rl_challenge

Learn Deep Reinforcement Learning in depth in 60 days

72crm-java

The JAVA version for "72CRM"

a-music-taste-analysis-using-spotify-api-and-python.

Exploring Audio Features and building a Machine Learning Approach

a-musicview

原创自定义控件之-Canvas实时绘制音乐波形图

a-nightmare-on-elm-street

It's a nightmare learning Elm Street - Music player

a-recommender-system-with-clustering-and-classification

In this project, we built up a recommender system based on the music data provided by Spotify. By using the system, the music provider can predict and then offer the appropriate songs to their users based on the characteristics of the music that has been heard previously.