1. Presented by Steven Lewis
“Comparative Analysis of Music Recordings from Western and Non-Western traditions by Automatic Tonal Feature Extraction”
Paper by Emilia Gómez, 2008
Presented by Steven Lewis
ISE-575, Spring 2011

2. Introduction and Motivation
Music Information Retrieval technology was originally developed for use on western music, which is only a fraction of the world’s music.
Can these analysis techniques work for non-western music as well?
The challenge undertaken was to automatically distinguish western from non-western music

3. Western music Refers to music of European origin
Written on the staff notation
Classified by genre
Classical, Rock, Jazz, Disco…

4. Non-Western music Classified by geographical region Asia Africa
Native American
Aboriginals
Arabs
Prehistoric tribes
etc.

5. A Few of the Musical Differences
Western scales
Whole and half-steps
12 pitches per octave
Equal-tempered
Non-Western scales
Semi/quarter-tone intervals
More/less pitches per octave

6. The Tested Data Set
1,000 songs
500 songs

7. Methodology Overview Tonal analysis Examined only the first 30 seconds
Independent of instruments and tempo
Examined only the first 30 seconds
Saves on computation time
key algorithm has similar accuracy over the first 15 sec vs analyzing the entire piece

8. Feature 1: Tuning frequency
Not assumed to be universal (A=440Hz)
Chosen so the spectral peaks fit closest within an equal-tempered scale

9. Tuning Frequency Distribution
(x-axis centered around 440Hz, measured in cents)

10. Feature 2: Pitch class distributions

11. Intensity of 2nd Degree in Diatonic Scale

12. Distribution of Equal-tempered deviation

13. Feature 3: Roughness Measure of sensory dissonance
Non-Western instruments sometimes are slightly mistuned on purpose

14. Machine Learning: Decision Trees

15. Machine Learning: SVM Support Vector Machines
Finds the optimal characteristics to globally maximize the separation between two classes
Trained on a sample data set to minimizes the classification errors for unseen samples

16. Classification Results
Precision: “fraction of the retrieved instances that belong to the correct category”
Recall: “fraction of the documents that belong to the correct category which are
successfully retrieved”

17. Western Classification Accuracy
Electronica was least accurate category, due to the frequent occurrence of “non-quantized-pitched sounds”

18. Non-Western Classification Accuracy
Arabic was the least accurate category, due to high similarity with Western music in the use and emphasis of the 2nd, 4th, and 5th scale degrees

19. Independent Test Set Results
Voyager Golden Record (12 W, 17 N/W)

20. Conclusions
Audio analysis technology was successfully applied to both Western and Non-Western music.
Demonstrated 80% classification accuracy for 1,500 pieces using a limited set of extracted audio features (no timbre or rhythm analysis)

21. Final Thoughts
The world continues to grow more connected, and the spread of musical ideas is breaking free from geographical proximity. The longstanding classification of music into the Western and Non-Western is called into question when considering the future relevance of a musical world defined in terms of physical boundaries that no longer exist…