1. The Greek Audio Dataset
Dimos Makris, Katia Lida Kermanidis, and Ioannis Karydis
Dept. Of Informatics, Ionian University

2. Music Information Retrieval
Musical data
acoustic, i.e. sound recordings
symbolic, i.e. sheet music
associated information to the musical content
metadata, social tags
Required for testing
efficiency & effectiveness of the methods
comparison of existing methods
show improvement

3. Nature of music Highly artistic Local music
ornamentation
personal expression during performance
adaptation
Local music
numerous differences from pop mainstream
different instruments & rhythms
Methods’ application results
not always intuitive
MIR methods
require all kinds of music

4. Intellectual property
Existing datasets
Intellectual property
issues

5. The Greek Audio Dataset
Freely-available collection of Greek musical data
for the purposes of MIR
Each song contains
audio features
immediate use in MIR tasks
lyrics of song
manually annotated mood & genre labels
YouTube link
further feature extraction

6. Greek music Greek musical tradition Greek contemporary music
diverse and celebrated
Greek contemporary music
Greek traditional music
Byzantine music
Greek traditional (folk) music
Combination of songs, tempos and rhythms from a litany of Greek regions
basis for the Modern Greek traditional music scene

7. Dataset creation process
Selection of the music tracks
aiming to make the set balanced
Sources from
personal CD collections
Audio feature extraction
jAudio
Lyrics
various sources
YouTube link selection criteria
number of views, number of responses, best audio quality, audio similarity to CD

8. Genre classification Greek musical culture oriented tags
Rembetiko, Laiko, Entexno, Modern Laiko, Rock, Hip-Hop/R & B, Pop, Alternative
Genre assignment
Listening tests per song
Class
# of tracks
Rempetiko 65
Laiko
186
Entexno
195
Modern laiko
175
Rock
Hip/hop 60
Pop 63
Alternative 61

9. Mood classification 5 annotators per song
Thayer model - 16 Mood taxonomies
valence & arousal
2-dimensional emotive plane into 4 parts by having positive/high and negative/ low values respectively
Arousal -> linked to energy
moods range from “angry” & “exciting” to “tired” & “serene”
Valence -> linked to tension
moods range from “sad” & “upset” to “happy” & “content”

10. GAD content 1000 songs For each song A total of 277 unique artists
its lyrics
a YouTube link
A total of 277 unique artists
The accumulated lyrics contain:
32024 lines
words
characters

11. GAD availability http://di.ionio.gr/hilab/gad two formats HDF5 CSV
efficient for handling the heterogeneous types of information
audio features in variable array lengths
names as strings
easy for adding new type of features
CSV
compatible for processing with
Weka, RapidMiner & similar data mining platforms

12. Acoustic features Based on timbre, rhythm & pitch.
Includes derived features
application of meta-features to primary features
Timbral Texture Features
used to differentiate mixture of sounds based on their instrumental compositions
FFT, MFCC, Spectrum, Method of Moments (MoM)
Rhythm Features:
used to characterize regularity of rhythm, beat, tempo
Beat, Freq, Beat Histogram
Pitch Content Features:
describe the distribution of pitches
Linear Predictive Coding (LPC)
MoM: Method Of Moments.
LPC: Linear Predictive Coding

13. Centroid & Rolloff for genres Rock and Entexno

14. Future Direction of the Dataset
Inclusion of user generated tags
from tagging games or web-services
Increase of labels for mood and genre
more users
Expansion of the number of songs
include more & latest top-chart songs
Genres’ refinement
addition of detailed labels with descriptions
Content balancing
in terms of moods and/or genres,
Inclusion of scores
Development of programming language wrappers

15. The Greek Audio Dataset
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