Creating Emotional Music with AI and Deep Learning

Table of Contents

1. Introduction
2. Emotion Based Music Recommender Project Overview
3. Understanding Emotion Detection Logic
   1. Utilizing the Live Emoji Prediction Code
   2. Data Collection
   3. Data Training
   4. Inference
4. Creating the Emotion Music Project
   1. Project Setup
   2. Creating the UI
   3. Opening a WebRTC Streamer
   4. Processing Frames with Emotion Detection
   5. Opening a YouTube Tab with Recommended Songs
   6. Managing Camera Operation
5. Conclusion

Emotion Based Music Recommender: A Deep Dive into Emotion Detection and Song Recommendations

In this article, we will explore the fascinating world of emotion-based music recommendation and learn how to Create a project from scratch. The project, titled "Emotion Based Music Recommender," aims to predict the user's emotion using their preferred language and favorite singer, and recommend songs based on that emotion. We will dive into the code and step-by-step process of creating this project, leveraging the concept of live emoji prediction and the power of deep learning.

Introduction

Emotions play a crucial role in our lives, influencing our mood and mindset. Music, on the other HAND, has the ability to Evoke strong emotional responses, making it an ideal medium for expressing and experiencing emotions. With the advancement of technology, we now have the opportunity to create personalized playlists based on our emotions. In this article, we will explore how to build an emotion-based music recommender using machine learning and data science techniques.

Emotion Based Music Recommender Project Overview

The Emotion Based Music Recommender project aims to create a system that can predict a user's emotion based on their preferred language and favorite singer. By analyzing the user's facial expression, the system will determine the emotion and recommend songs that match the detected emotion. Through this project, users will be able to explore and discover music that resonates with their Current emotional state.

Understanding Emotion Detection Logic

Before we dive into the code and implementation details, let's understand the logic behind emotion detection. In this project, we will utilize the code from a previous project called "Live Emoji Prediction." This code contains the necessary logic and instructions for data collection, training, and inference. By reusing this code, we can create a model capable of detecting various emotions such as happiness, sadness, anger, surprise, and more.

Utilizing the Live Emoji Prediction Code

To begin, we will download the code for the "Live Emoji Prediction" project. This code includes detailed explanations and demonstrations of data collection, training, and inference. By following the instructions provided in the code, we can create and train our emotion detection model. We'll then use this model as the foundation for our Emotion Based Music Recommender project.

Data Collection

Data collection is a crucial step in training an emotion detection model. In this project, we will collect data for different emotions, such as happiness, sadness, anger, surprise, and more. The collected data will serve as the training dataset for our model. By capturing facial expressions associated with each emotion, we can create a comprehensive dataset that covers a wide range of emotional states.

Data Training

Once we have collected the necessary data, we can proceed with training the emotion detection model. Using the "Live Emoji Prediction" code, we will train our model using the collected dataset. This training step will involve processing the images of facial expressions and training the model to recognize and classify different emotions.

Inference

After training the model, we can perform inference to test its accuracy and performance. With the model in place, we can now predict the user's emotion based on their facial expression. By inputting a frame or image of the user's face, the model will generate a prediction for the detected emotion. This prediction will then be used to recommend songs that Align with the user's emotional state.

Creating the Emotion Music Project

Now that we have a clear understanding of the logic behind emotion detection, let's proceed with building the Emotion Based Music Recommender project. In this section, we will cover the step-by-step process of creating the project from scratch.

Project Setup

To begin, we need to set up the project folder structure and clone the necessary code repositories. By following the provided instructions, we can create a blank folder and clone the required code repositories. This will provide us with the necessary codebase and resources to proceed with building the project.

Creating the UI

The first step in building our project is creating the user interface (UI). We will utilize the Streamlit library to create a simple yet interactive UI that allows users to input their preferred language and favorite singer. By implementing text input fields and a submit button, users will be able to provide their preferences for the music recommendation.

Opening a WebRTC Streamer

Once the user has inputted their language and singer preferences, we will open a WebRTC streamer to capture the user's facial expression. By leveraging the power of the streamlit-webrtc library, we can access the user's webcam and detect their facial landmarks. These landmarks will serve as input for our emotion detection model.

Processing Frames with Emotion Detection

In this step, we will utilize the previously trained emotion detection model to process the frames captured by the WebRTC streamer. By applying the model to each frame, we can detect the user's emotion in real-time. We will use the MediaPipe library to detect the facial landmarks and hands, allowing us to draw the landmarks on the frame for better visualization.

Opening a YouTube Tab with Recommended Songs

Once we have detected the user's emotion, we can open a new tab in the web browser and search for recommended songs based on the detected emotion, preferred language, and favorite singer. By using the webbrowser library, we can programmatically open a YouTube search URL with the appropriate query parameters. This will allow us to display a list of recommended songs for the user to explore.

Managing Camera Operation

To provide a seamless user experience, we need to manage the camera operation. When the user inputs their preferences and clicks the "Recommend Me Songs" button, the camera should stop capturing frames and making predictions in the background. By leveraging session state variables, we can control the camera operation and ensure it only runs when necessary. This will optimize the performance of our project and improve the user interface.

Conclusion

In this article, we explored the concept of emotion-based music recommendation and learned how to create a project from scratch. By leveraging the power of deep learning and machine learning, we can detect the user's emotion based on their facial expressions and recommend songs that align with their emotional state. The Emotion Based Music Recommender project offers a personalized and immersive music experience, allowing users to discover new songs that resonate with their emotions. By following the step-by-step guide provided in this article, You can create your own emotion-based music recommender and unlock the potential of personalized music playlists.