Streamlining Python Development: A Guide to a Modern Project Setup
Music Mood Detection (Lyrics based Approach)
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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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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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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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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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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