A presentation on Music Mood detection techniques including information retrieval through audio features and lyrical features.

1. Presented by:
Akhil H. Panchal
T.E. Computer
Guided by:
Prof. Mrs. Tiple
Computer Dept.
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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
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3. EMOTION!
• Reactions to an
event or a
stimulus that
lasts for a short
period of time.
• Important
concern for
Music
psychologists.
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4. MOOD!
• A generalized
form of your
emotional
feelings that last
for a longer
period of time.
• Less intense.
• Important
concern for MIR
researchers!
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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.
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6. MOOD MODELS!
 A way to classify various moods so
that each mood can be identified
distinctively.
Mood
Models
Categorical
Dimensional
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7. HEVNER’S MODEL
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8. RUSSELL’S MODEL
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9. THAYER’S MODEL
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10. NAVRAS :
INDIAN CLASSICAL MODEL
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11. HOW?
Music Mood can be detected
by 2 main techniques.
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12. AUDIO FEATURES
2-tier taxonomy of
Music Features:
Low Level
Time Signature
Tempo(BPM)
Timbral Temporal
Mid &
High
level
Pitch
Rhythm
Harmonies
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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.
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14. LIST OF FEATURES
Spectral
Centroid
Spectral
Flux
Mel-
frequency
Coefficients
Roll-off
point
Zero-
crossings
Beat
Histogram
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15. Conversion of Hertz into Mel scale:
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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.
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 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.
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18. LYRICS FEATURES
Text
Stylistic
N-gram
content
words
POS(Part of
Speech)
ANEW &
WordNet
General
Enquirer
LYRICS BASED APPROACH
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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‟)
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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)
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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
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22. ANEW & WordNet
 ANEW has 1034 English words with
scores in 3 dimensions:
 Arousal
 Valence
 Dominance
 Extended by adding synonyms
from WordNet & WordNet-affect.
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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.
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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.
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25. CURRENT MMD PLATFORMS
 Stereomood.com
 Musicovery.com
 Mymusicsource.com
 Last.fm
 Youlicense.com
 Crayonroom.com
 Googlemusic.com (China)
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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.
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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.
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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.
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30. ♫Q & A♫
THANK YOU!
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