@@ -110,7 +110,8 @@ Developing a database of music tracks categorized based on their mood states.
3. The proposal is to provide an overview of how music can affect the user's mood and how to choose the right music tracks to improve the user's moods, instead of manual sorting and playing.
> Nature of solution
> Nature of solution
The proposed system benefits us to present interaction between the user and the music player. The
purpose of the system is to capture the face properly with the camera. Captured images are fed into the
Convolutional Neural Network which predicts the emotion. Then emotion derived from the captured
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@@ -119,22 +120,20 @@ automatically to change the user's moods, which can be happy, sad, natural, or s
system detects the emotions, if the topic features a negative emotion , then a selected playlist is going
to be presented that contains the foremost suitable sorts of music that will enhance the mood of the
person positively. Music recommendation based on facial emotion recognition contains four modules.
• Face emotion recognition based mood detection:- Implementation of an emotion classifier
using opencv and scikit-learn. It uses a support vector machine (svm) classifier to recognize
mood based on images of faces.
• Mood detection with voice recognition techniques:- he algorithm uses a neural network to
classify mood in speech, using the Ravdess emotional speech audio dataset as training data. The
input to the model is a voice sample, which is pre-processed to extract features, and the output
is the detected mood.
• Create song playlist on user wish list using machine learning techniques:- Recommend songs to
a user based on their preferences using collaborative filtering. The process involves building a
dataset of user-song ratings and using this data to train the collaborative filtering algorithm.
• Song mood classification with base and frequency:- Classify songs based on their audio features
using a neural network for multi-class classification. The process involves building a dataset of
songs and using their audio features as inputs to the neural network, which will output the
classification of the songs.
* • Face emotion recognition based mood detection:- Implementation of an emotion classifier
* using opencv and scikit-learn. It uses a support vector machine (svm) classifier to recognize
* mood based on images of faces.
*
* • Mood detection with voice recognition techniques:- he algorithm uses a neural network to
* classify mood in speech, using the Ravdess emotional speech audio dataset as training data. The
* input to the model is a voice sample, which is pre-processed to extract features, and the output
* is the detected mood.
*
* • Create song playlist on user wish list using machine learning techniques:- Recommend songs to
* a user based on their preferences using collaborative filtering. The process involves building a
* dataset of user-song ratings and using this data to train the collaborative filtering algorithm.
*
* • Song mood classification with base and frequency:- Classify songs based on their audio features
* using a neural network for multi-class classification. The process involves building a dataset of
* songs and using their audio features as inputs to the neural network, which will output the
