Automatic Music Playlist Generation based on Human Emotion

The chapter 1 introduces the background of Emotion Detection Media Player, which aims to understand the functioning of three algorithms used to develop this media player. The main objective is to create a system that detects human emotions and generates playlists automatically based on these emotions. The research uses edge detection algorithm to detect digital image edges, face detection algorithm for facial expression recognition, and SVM for image classification based on features and behavior. The goal is to reduce the time spent creating playlists manually and provide an efficient way to update them.

Ch 1: Introduction
1.1 Background of the paper
Music has some important role in enhancing the life of a person because it is highly significant
relaxation medium for music follower as well as listener. Now a day, the music technology is
very improved and the music listener uses this improved technology such as local playback,
multicast stream, reverse and other facilities. The listeners are satisfied by these factors and plays
music based on their mood as well as behaviour. Audio feeling recognition provides list of music
that is supported to various mood and emotions of music lover. Various categories of emotions
and audio signal that is received, is classified by audio felling recognition. To explore some
features of audio, AN signal is used and MR is used to extract various important data from the
AN signal of audio. The listener is trying to set their play list according to their mood. However,
it consumes more time. Various music players provide different kinds of features such as proper
lyrics with singer name and all. In this system, the arrangement of playlist is reacted based on
listener’s emotions to save the time for storing playlist in manual manner.
The best way to express emotion and mood of person is the facial expression and physical
gesture of human. This system is used extract facial expression and based on this expression the
system automatic generate playlist that is completely matched with mood and behaviour of
listeners. This system consumes less time, reduces the cost hardware and removes the overheads
of memory. The face expression is classified into five different parts like joy, surprise,
excitement, anger and sadness of human. To extract important, related data from any audio
signal, high accurate audio extraction technology is used with lesser time. A model of emotion is
expressed to classify the song into seven types such as unhappy, anger, excitement with joy, sad-
anger, joy, surprise. The emotion-audio module is combination of feeling extraction module and
module of audio feature extraction. This mechanism provides a more huge potentiality and good
real time performance rather than recent methodology. Various kinds of approaches are used to
process the edge detection of an image such as mouse, speech reorganization using AI and
others. In this case, three algorithms are used these are used like edge detection, face detection
and SVM. In the edge detection algorithm helps to reduce computation of processing any large
image. Face detection algorithm provides high accuracy to detect object as well as image. This
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algorithm is very fast in nature. This system can extract emotion and mood of human and they
started communication with human being. This system is used to sense facial expression of
human and based on this facial expression it arrange the playlist for human. Human do not need
to select their playlist based on their mood or emotion. Computer is able to communicate with
human being like talking, reacting and extracting human emotion, automatically guess the
feelings of human being. The emotion detection develops new technology in recent years to
process various image, interaction between human and machine and machine learning. Now a
day, emotion detection plays a vital role in neuroscience, computer science, medical and other
various purposes. In order to make rational decision, interaction with social media, emotion
detection is very important. This system can understand what listener wants to listen according to
their current state of mind.
1.2 Aim of the research
The aim of the research is to understand the functioning of the three algorithms used in order to
develop the Emotion detection Media player.
1.3 Objectives of the research
The objectives of the research are listed below:
 To play the song automatically based on the human emotions and feelings.
 To discuss about three algorithms for detecting the emotion of humans
 To communicate with the people and extract the gesture and mind state of people
 To discuss the impact of three algorithms on emotion detection media player
 To develop proper algorithm for recognizing human emotion
 To develop the appropriate system as per the human emotion this is used to identify
human gesture using some technology.
1.4 Research questions
 How to play the song automatically as per the listener’s mood?
 What is main purpose of the three algorithms to detect emotion of human?
 How to extract the people emotion and gesture of human using media player?
 What is the impacts of thee algorithms on emotion detection media player?
 How to develop appropriate algorithm for detecting emotion?
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 How to develop appropriate media player to extract emotion of human?
1.5 Significance of the paper
The importance of emotion detection media player is looking forward to the emotion of users
and it is used to detect human emotion. This system helps to play music automatically by
understanding the emotion of human. It reduces the time of computation and searching time for
human. Memory overhead is reduces by this emotion detection media player. The system
provides accuracy and efficient result for detecting gesture of human. It recognizes the facial
expression of people and arranges suitable playlist for them. It mainly focuses on the features of
the detected emotion rather than actual image. Here, listener does not want choose any song from
the playlist automatically. There are no requirements of any playlists. The music lovers do not
want to categorize the song according to their gesture, mental state and feelings. Here SVM
algorithm is used to classify and analysis the regression. It uses hyper-plane for doing this. It is
used to access image with the help computer devices such as mouse and various computer
network. It mainly detects the digital image through the video. It classifies data as per their
behaviour and features. Data is transformed by using this technique and according to this
collected data set; it set some boundaries among the promising outcomes. Another algorithm is
edge detection algorithm. In any image the edge parts is the basic thing. The edge is the set of
several pixels. The detection of edge is one of the best techniques to extract the edge of an
image. The output of this algorithm is expressed by the two dimensional function. The third
algorithm that is used for emotion detection is the face recognition. The main advantage of this
algorithm is to identify the actual emotion of human being by their facial expression such as joy,
sadness, anger and other emotions. The algorithm is mainly responsible to reduce the
computation time for detecting the facial expression of any person.
1.6 Problem statement
This is difficult task for music lover to create and segregate the manual playlist among the huge
number of songs. It is quite impossible to keep the track of large number of song in the playlist.
Some songs that are not used for long time waste lot of space and listener have to delete this kind
of song manually. Listeners have some difficulties to identify the suitable song and play the song
based on variable moods. Now a day, people cannot automatically change or update the playlist
in a easiest way. Listener has to update this playlist in a manual manner. The list of playlist is not
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equal all time as per the listener requirement. It changes frequently. So there is no such system to
reduce these kind of problem.
1.7 Research limitation
In this research, there are some limitations in the used methodologies. Three types of algorithms
are used to design the system. There are some chances to use two or more algorithms to make the
new developed system efficient. However, this process takes more cost for using these
algorithms. It is very much time consuming task because the processing of each algorithm in a
very efficient way. For this reason, it is big limitation for this research. If the research gets much
time and cost then the research will be very effective and the new improved system will more
accurate to give the actual output.
1.8 summary
The chapter 1 depicts the background of the Emotion detection media player. The aim of this
research is to understand the functioning of the three algorithms used in order to develop the
Emotion detection Media player. The main objective of the research is to develop a new system
to detect the human emotion and based on this emotion this system generates the lists of song
automatically. For this reason, the system uses three algorithms. Edge detection algorithm is used
to detect the edge of the digital image, face detection algorithm is used to detect the facial
expression of the human, and SVM is used to classify the detected image as per the features and
behaviour.
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1.7 structure of the research project
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