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Presented by:
Akhil H. Panchal
T.E. Computer
Guided by:
Prof. Mrs. Tiple
Computer Dept.
1
CONTENTS
 Mood vs. Emotion
 Why MMD?
 Mood Models
 How MMD?
 Audio Features
 Hierarchical MMD algorithm
 Lyrics Features
 A Lyrics based approach to MMD
 Applications
 Limitations
2
EMOTION!
• Reactions to an
event or a
stimulus that
lasts for a short
period of time.
• Important
concern for
Music
psychologists.
3
MOOD!
• A generalized
form of your
emotional
feelings that last
for a longer
period of time.
• Less intense.
• Important
concern for MIR
researchers!
4
WHY MMD?
 Need for sorting the ever increasing Music
Database according to our choice(mostly
being “Mood”).
 Time consuming for Listeners to manually
select songs suiting a particular mood or
occasion.
 Huge variety of our Music ranging from
various Albums/Artists/Composers which is
heavily influenced by mood.
5
MOOD MODELS!
 A way to classify various moods so
that each mood can be identified
distinctively.
Mood
Models
Categorical
Dimensional
6
HEVNER’S MODEL
7
RUSSELL’S MODEL
8
THAYER’S MODEL
9
NAVRAS :
INDIAN CLASSICAL MODEL
10
HOW?
Music Mood can be detected
by 2 main techniques.
11
AUDIO FEATURES
2-tier taxonomy of
Music Features:
Low Level
Time Signature
Tempo(BPM)
Timbral Temporal
Mid &
High
level
Pitch
Rhythm
Harmonies
12
AUDIO FEATURES
 Low-level features not closely related to the
properties perceived by ‘listeners’.
 Mid-level features derived from low-level
features help in extracting properties of
Music closely perceived by ‘listeners’ as
Mood.
14
LIST OF FEATURES
Spectral
Centroid
Spectral
Flux
Mel-
frequency
Coefficients
Roll-off
point
Zero-
crossings
Beat
Histogram
15
Conversion of Hertz into Mel scale:
16
C=1127.01048
HEIRARCHICAL MUSIC MOOD
DETECTION ALGORITHM
1. Start.
2. Convert Music clip into uniform format.
3. Divide Music clip into plurality of frames.
4. Extract Audio features: Spectral features, Beat
histogram, Mel-frequency coefficients.
5. Calculate average frame intensities.
19
 Based on Thayer‟s Mood Model
 Used for classifying a music clip into either
of the 4 categories: G1(Exuberance,
Anxious),G2(Contentment & depression).
 Algorithm:
HEIRARCHICAL MUSIC MOOD
DETECTION ALGORITHM
6. Classify Music clip into a mood group based on
intensity feature.
a) Determine probabilities of 1st n 2nd group
based on intensity.
b) If P(G1)>P(G2) then select G1.
Else select G2.
7. Classify Music clip into exact Music mood
based on timbral & rhythm features.
a) Determine probabilities of 1st n 2nd group
based on intensity.
b) If P(M1)>P(M2) then select M1
Else select M2.
20
LYRICS FEATURES
Text
Stylistic
N-gram
content
words
POS(Part of
Speech)
ANEW &
WordNet
General
Enquirer
LYRICS BASED APPROACH
21
TEXT STYLISTIC FEATURES
 Include text statistics such as:
 No. of unique words
 No. of unique lines
 No. of repeated lines/words
 Words per minute
 Special punctuation marks(!) &
 Interjection words (e.g.: „Hey‟, „Oh‟)
22
PART OF SPEECH (POS)
FEATURES
 Grammatical tagging of words
according to their definition and the
textual context they seem in.
 E.g.: Time flies like an arrow.
(noun) (verb)(prep.)(art.) (noun)
23
N-GRAM CONTENT WORDS
 Combination of unigrams, bigrams
& trigrams of content words.
 Help in detecting emotion.
Happy Romantic Aggressive Hopeful
Heaven With you I‟ve never If you
All around Love Kill Dreams
24
ANEW & WordNet
 ANEW has 1034 English words with
scores in 3 dimensions:
 Arousal
 Valence
 Dominance
 Extended by adding synonyms
from WordNet & WordNet-affect.
25
LYRICS BASED MOOD
DETECTION SYSTEM
 The lyrics of the song are given as
input in textual form.
 Lyrics pre-processing is performed.
 Intro, Verses, Chorus are detected at
this stage.
 Instructions like „repeat chorus‟ are
replaced by the actual lyrics.
 Spelling errors are corrected.
26
LYRICS BASED MOOD
DETECTION SYSTEM
 Lyrical features mentioned are
extracted (with help of ANEW,
WordNet)
 The song is tagged with various
moods with varying probabilities.
 The mood tagged with maximum
probability is selected as the mood of
the music clip.
27
CURRENT MMD PLATFORMS
 Stereomood.com
 Musicovery.com
 Mymusicsource.com
 Last.fm
 Youlicense.com
 Crayonroom.com
 Googlemusic.com (China)
28
29
APPLICATIONS
 Shop owners seeking music to attract
certain clients.
 Sorting the music that we have
according to a certain mood or
occasion.
 Ad films requiring a highly
memorable & positive emotion
invoking music for their products.
30
APPLICATIONS
 A Disk Jockey seeks Music having the
same beat & a similar mood as the
current song.
 In games, to invoke moods such as
excitement, danger, fear, victory &
happiness.
 A call center asking the callers to
hold, need happy music pieces.
31
LIMITATIONS
 Precision issues in case of
metaphors.
 Mood from some Music pieces can
be subjective.
 Mood perceived highly dependent
on cultural background.
 Conversion to standard format leads
to loss of certain features.
32
♫Q & A♫
THANK YOU!
33

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Music Mood Detection (Lyrics based Approach)

  • 1. Presented by: Akhil H. Panchal T.E. Computer Guided by: Prof. Mrs. Tiple Computer Dept. 1
  • 2. CONTENTS  Mood vs. Emotion  Why MMD?  Mood Models  How MMD?  Audio Features  Hierarchical MMD algorithm  Lyrics Features  A Lyrics based approach to MMD  Applications  Limitations 2
  • 3. EMOTION! • Reactions to an event or a stimulus that lasts for a short period of time. • Important concern for Music psychologists. 3
  • 4. MOOD! • A generalized form of your emotional feelings that last for a longer period of time. • Less intense. • Important concern for MIR researchers! 4
  • 5. WHY MMD?  Need for sorting the ever increasing Music Database according to our choice(mostly being “Mood”).  Time consuming for Listeners to manually select songs suiting a particular mood or occasion.  Huge variety of our Music ranging from various Albums/Artists/Composers which is heavily influenced by mood. 5
  • 6. MOOD MODELS!  A way to classify various moods so that each mood can be identified distinctively. Mood Models Categorical Dimensional 6
  • 11. HOW? Music Mood can be detected by 2 main techniques. 11
  • 12. AUDIO FEATURES 2-tier taxonomy of Music Features: Low Level Time Signature Tempo(BPM) Timbral Temporal Mid & High level Pitch Rhythm Harmonies 12
  • 13. AUDIO FEATURES  Low-level features not closely related to the properties perceived by ‘listeners’.  Mid-level features derived from low-level features help in extracting properties of Music closely perceived by ‘listeners’ as Mood. 14
  • 15. Conversion of Hertz into Mel scale: 16 C=1127.01048
  • 16. HEIRARCHICAL MUSIC MOOD DETECTION ALGORITHM 1. Start. 2. Convert Music clip into uniform format. 3. Divide Music clip into plurality of frames. 4. Extract Audio features: Spectral features, Beat histogram, Mel-frequency coefficients. 5. Calculate average frame intensities. 19  Based on Thayer‟s Mood Model  Used for classifying a music clip into either of the 4 categories: G1(Exuberance, Anxious),G2(Contentment & depression).  Algorithm:
  • 17. HEIRARCHICAL MUSIC MOOD DETECTION ALGORITHM 6. Classify Music clip into a mood group based on intensity feature. a) Determine probabilities of 1st n 2nd group based on intensity. b) If P(G1)>P(G2) then select G1. Else select G2. 7. Classify Music clip into exact Music mood based on timbral & rhythm features. a) Determine probabilities of 1st n 2nd group based on intensity. b) If P(M1)>P(M2) then select M1 Else select M2. 20
  • 18. LYRICS FEATURES Text Stylistic N-gram content words POS(Part of Speech) ANEW & WordNet General Enquirer LYRICS BASED APPROACH 21
  • 19. TEXT STYLISTIC FEATURES  Include text statistics such as:  No. of unique words  No. of unique lines  No. of repeated lines/words  Words per minute  Special punctuation marks(!) &  Interjection words (e.g.: „Hey‟, „Oh‟) 22
  • 20. PART OF SPEECH (POS) FEATURES  Grammatical tagging of words according to their definition and the textual context they seem in.  E.g.: Time flies like an arrow. (noun) (verb)(prep.)(art.) (noun) 23
  • 21. N-GRAM CONTENT WORDS  Combination of unigrams, bigrams & trigrams of content words.  Help in detecting emotion. Happy Romantic Aggressive Hopeful Heaven With you I‟ve never If you All around Love Kill Dreams 24
  • 22. ANEW & WordNet  ANEW has 1034 English words with scores in 3 dimensions:  Arousal  Valence  Dominance  Extended by adding synonyms from WordNet & WordNet-affect. 25
  • 23. LYRICS BASED MOOD DETECTION SYSTEM  The lyrics of the song are given as input in textual form.  Lyrics pre-processing is performed.  Intro, Verses, Chorus are detected at this stage.  Instructions like „repeat chorus‟ are replaced by the actual lyrics.  Spelling errors are corrected. 26
  • 24. LYRICS BASED MOOD DETECTION SYSTEM  Lyrical features mentioned are extracted (with help of ANEW, WordNet)  The song is tagged with various moods with varying probabilities.  The mood tagged with maximum probability is selected as the mood of the music clip. 27
  • 25. CURRENT MMD PLATFORMS  Stereomood.com  Musicovery.com  Mymusicsource.com  Last.fm  Youlicense.com  Crayonroom.com  Googlemusic.com (China) 28
  • 26. 29
  • 27. APPLICATIONS  Shop owners seeking music to attract certain clients.  Sorting the music that we have according to a certain mood or occasion.  Ad films requiring a highly memorable & positive emotion invoking music for their products. 30
  • 28. APPLICATIONS  A Disk Jockey seeks Music having the same beat & a similar mood as the current song.  In games, to invoke moods such as excitement, danger, fear, victory & happiness.  A call center asking the callers to hold, need happy music pieces. 31
  • 29. LIMITATIONS  Precision issues in case of metaphors.  Mood from some Music pieces can be subjective.  Mood perceived highly dependent on cultural background.  Conversion to standard format leads to loss of certain features. 32
  • 30. ♫Q & A♫ THANK YOU! 33