Emotion Based Music Player System

ABSTRACT: –

Face Emotion Recognition is a process of image processing. Facial Emotion Recognition is the process of converting the movements of a person’s face into a digital database using various images processing technique. This database may then be used to produce CG, (computer graphics) computer animation for movies, games, or real-time avatars. Facial Emotion Recognition recognizes the face emotions. Recently, a number of new technologies for Facial Expression Recognition have been developed. This technique with equal contribution from AI filed can revolutionize the robotic industry. Human interaction with robots will reach completely new level as robots will be able to understand Human emotions & on the basis of those emotions a music player will be play.

Submit objectives of the project highlighting its importance:

• The primary goal of our project is to design, implement and evaluate a novel facial expression recognition system.
• The main objective of our project is to recognize different emotions of human and play the song according to the user’s detected emotion or mood.
• The other objective of our project is that different human emotions and expressions can be thoroughly understood by the robotic world in order to establish effective communication with the robots.

SYSTEM:-

the emotion based music player system is an application that uses machine learning algorithms to detect the users emotions and play music that matches their mood the system analyzes the users facial expressions and body language using computer vision techniques and interprets them to determine their emotional state it then selects music that corresponds to that emotion and plays it through the application

here are some key components of the system

• emotion detection module: this module uses computer vision algorithms to detect the users facial expressions and body language to determine their emotional state it analyzes the data from the camera and processes it to identify key features such as eye movements eyebrow position and mouth shape it then interprets these features to determine the users emotional state such as happy sad angry or calm.
• music selection module: this module uses machine learning algorithms to select music that corresponds to the users emotional state it analyzes the users emotional state and selects music that matches that emotion the module may use features such as tempo rhythm and tone to match the users mood.
• music playback module: this module plays the selected music through the application it may include controls such as play pause and skip as well as volume and playback speed controls.
• user interface module: this module provides the user interface for the application it may include features such as music search playlists and favorite songs it may also include a feedback mechanism that allows users to rate the music and provide feedback on its appropriateness for their current emotional state.
• machine learning model training module: this module trains the machine learning algorithms used in the emotion detection module and the music selection module it may use large datasets of facial expressions and music genres to improve the accuracy of the system.

the emotion based music player system provides an innovative way for users to listen to music that matches their mood it can be especially useful for individuals who have difficulty identifying and expressing their emotions or who want to enhance their emotional well-being through music

PROPOSED SYSTEM:-

This application uses face detection and mood recognition to determine the user’s mood and based on this, it gives a personalized play list. The face detection algorithm is based on OpenCV library and the mood detection part will be based on pattern matching. These implementations are designed in order to generate a playlist according to the user moods and offer these functionalities:

1. Set your mood & play songs.
2. Get your mood automatically by analyzing a periodical camera capture.

MODULES:-

• Data Collection Module: This module is responsible for collecting the necessary data to train the machine learning algorithms. It may include collecting data on facial expressions, body language, and music preferences.

• Emotion Detection Module: This module analyzes the user’s facial expressions and body language to determine their emotional state. It may use computer vision techniques such as face detection, facial landmark detection, and emotion recognition algorithms to determine the user’s mood.

• Music Selection Module: This module uses machine learning algorithms to select music that corresponds to the user’s emotional state. It may use features such as tempo, rhythm, and tone to match the user’s mood. The module may also include a recommendation system that suggests new songs or artists based on the user’s music preferences.

• Music Playback Module: This module is responsible for playing the selected music through the application. It may include controls such as play, pause, and skip, as well as volume and playback speed controls.

• User Interface Module: This module provides the user interface for the application. It may include features such as music search, playlists, and favorite songs. It may also include a feedback mechanism that allows users to rate the music and provide feedback on its appropriateness for their current emotional state.

• Machine Learning Model Training Module: This module trains the machine learning algorithms used in the Emotion Detection Module and the Music Selection Module. It may use large datasets of facial expressions and music genres to improve the accuracy of the system.

• Data Visualization Module: This module provides visualizations of the data collected by the system. It may include graphs or charts that show the user’s emotional state over time or the distribution of music genres played by the system.

• Data Storage Module: This module is responsible for storing the data collected by the system. It may include a database or file system that stores the user’s emotional state, music preferences, and other relevant data.

These modules work together to create an Emotion Based Music Player System that provides a personalized music experience for the user based on their emotional state.

APPLICATION:-

• Emotion Detection: The application should be able to detect the user’s emotional state based on their facial expressions and body language. It could use computer vision techniques such as face detection and emotion recognition algorithms to determine the user’s mood.

• Music Selection: Once the user’s emotional state is detected, the application should select music that matches their mood. It could use machine learning algorithms to analyze the tempo, rhythm, and tone of the music to find the best match for the user’s mood.

• Music Playback: The application should have a music player that can play the selected music. It could include features such as play, pause, and skip, as well as volume and playback speed controls.

• Music Recommendations: The application could include a recommendation system that suggests new songs or artists based on the user’s music preferences and emotional state.

• Playlist Creation: The application could allow the user to create playlists based on their emotional state or music preferences. It could also include pre-made playlists that are tailored to specific moods.

• Feedback Mechanism: The application could include a feedback mechanism that allows users to rate the music and provide feedback on its appropriateness for their current emotional state. This feedback could be used to improve the accuracy of the system over time.

• Data Visualization: The application could provide visualizations of the user’s emotional state over time or the distribution of music genres played by the system. This could help the user understand their emotional patterns and music preferences.

Overall, the application should provide a personalized music experience for the user based on their emotional state, using machine learning algorithms to select the best music for their mood.

HARDWARE AND SOFTWARE REQUIREMENTS:-

HARDWARE:-

• Processor: Pentium 4
• RAM: 512 MB or more
• Hard disk: 16 GB or more
• Web Camera.

SOFTWARE:-

• JAVA JDK 1.7
• Net Beans 7.2
• OpenCV