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Presented by:Akhil H. PanchalT.E. Computer

MUSIC MOOD DETECTION:

Guided by:Prof. Mrs. Tiple

Computer Dept.

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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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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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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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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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MOOD MODELS!

A way to classify various moods so that each mood can be identified distinctively.

Mood Models

Categorical

Dimensional

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HEVNER’S MODEL

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RUSSELL’S MODEL

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THAYER’S MODEL

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NAVRAS : INDIAN CLASSICAL MODEL

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HOW?

MMD techniques

Audio Based Lyrics Based

Music Mood can be detected by 2 main techniques.

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AUDIO FEATURES

2-tier taxonomy ofMusic Features:

Low LevelTime Signature

Tempo(BPM)

Timbral Temporal

Mid & High level

PitchRhythm

Harmonies

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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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LIST OF FEATURES

Spectral Centroid

Spectral Flux

Mel-frequency

Coefficients

Roll-off point

Zero-crossings

Beat Histogram

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Conversion of Hertz into Mel scale:

𝑀𝐸𝐿=𝑐 . log (𝑓100

+1)

C=1127.01048

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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.

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:

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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 M1Else select M2.

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LYRICS FEATURES

Text Stylistic

N-gram content words

POS(Part of Speech)

ANEW & WordNet

General Enquirer

LYRICS BASED APPROACH

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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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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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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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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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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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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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CURRENT MMD PLATFORMS

Stereomood.com Musicovery.com Mymusicsource.com Last.fm Youlicense.com Crayonroom.com Googlemusic.com (China)

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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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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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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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♫Q & A♫

THANK YOU!