This document summarizes Rui Pedro Paiva's work on melody detection in polyphonic audio. It outlines an approach that uses multi-pitch detection to extract pitches from audio, then constructs pitch trajectories and segments them into notes. Melodic notes are identified by eliminating ghost notes, selecting salient notes, smoothing the melody, and removing accompaniment notes. The approach is evaluated on various datasets, achieving 81% pitch accuracy, 84.4% note accuracy, and 31-52.8% accuracy at discriminating melody from accompaniment. Future work could improve computational efficiency and melody/accompaniment separation.

1. Rui Pedro Paiva
Melody Detection in
Polyphonic Audio
9º Encontro da APEA
October 20, 2007
Centro de Informática e Sistemas da
Universidade de Coimbra

2. 2 Melody Detection in Polyphonic Audio
Outline
 Introduction
 Overview
 Multi-Pitch Detection
 From Pitches to Notes
 Identification of Melodic Notes
 Evaluation Procedures
 Overall Results
 Conclusions and Future Work

3. 3 Melody Detection in Polyphonic Audio
Introduction
 Motivation and Objectives
• Goal
- Extract a symbolic representation of melody from a polyphonic
audio musical signal
• Broad range of applications
- MIR, music education, plagiarism detection, metadata
• No general-purpose, robust, accurate solution
developed so far
 Overall Approach
• Focus on melody, no matter what other sources are
present (figure-ground separation)

4. 4 Melody Detection in Polyphonic Audio
Introduction
Melody Definition
• Subjective concept
- Different people may define and perceive melody in
different ways
- Proposals addressing cultural, perceptual, emotional
or musicological facets of melody
• Context of our work
- “Melody is the individual dominant pitched line in a
musical ensemble”

5. 5 Melody Detection in Polyphonic Audio
Overview
Raw Musical
Signal Melody Notes
Multi-Pitch
Detection
1) Cochleagram
2) Correlogram
3) Pitch Salience
Curve
4) Salient Pitches
Determination of
Musical Notes
1) Pitch Trajectory Construction
2) Trajectory Segmentation
(with onset detection directly
on the raw signal)
Identification of
Melodic Notes
1) Elimination of Ghost Octave
Notes
2) Selection of the Most Salient
Notes
3) Melody Smoothing
4) Elimination of False Positives
Pitches in 46-msec Frames Entire Set of Notes

6. 6 Melody Detection in Polyphonic Audio
Multi-Pitch Detection
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a) Sound waveform
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b) Cochleagram frame
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c) Correlogram frame
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d) Summary correlogram

7. 7 Melody Detection in Polyphonic Audio
From Pitches to Notes
Goal
• Quantize temporal sequences of detected
pitches into MIDI notes
Approach
• Pitch Trajectory Construction [Serra]
• Frequency-Based Track Segmentation
• Salience-Based Track Segmentation

8. 8 Melody Detection in Polyphonic Audio
From Pitches to Notes
Pitch Trajectory Construction
1 2 nn-1n-2
Frame Number
MIDInotenumber

9. 9 Melody Detection in Polyphonic Audio
From Pitches to Notes
Frequency-Based Track Segmentation
• Approximation of frequency curves by
piecewise-constant functions (PCFs)
- 1) MIDI quantization
- 2) Merging of PCFs: oscillation,
delimited sequences, glissando
- 3) Time correction
- 4) Assignment of final MIDI note numbers:
tuning compensationb) Filtered PCFs
a) Original PCFs

10. 10 Melody Detection in Polyphonic Audio
From Pitches to Notes
Frequency-Based Track Segmentation
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f[k](cents)
a) Eliades Ochoa's excerpt
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b) Female opera excerpt

11. 11 Melody Detection in Polyphonic Audio
From Pitches to Notes
Salience-Based Track Segmentation
- 1) Candidate Segmentation Points
- 2) Onset Detection on the audio signal
[Scheirer; Klapuri]
- 3) Validation of Segmentation Points by
Onset Matching
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Time (sec)
sF[k]

12. 12 Melody Detection in Polyphonic Audio
Identification of Melodic Notes
Goal
• Separate the melodic notes from the
accompaniment
Approach
• Elimination of Ghost Harmonically-Related
Notes
• Selection of the Most Salient Notes
• Melody Smoothing
• Elimination of Spurious Accompaniment
Notes
• Note Clustering

13. 13 Melody Detection in Polyphonic Audio
Identification of Melodic Notes
Elimination of Ghost Harmonically-
Related Notes
• Delete harmonically-related notes that satisfy
the “common fate” principle
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Time (msec)
NormalizedFrequency

14. 14 Melody Detection in Polyphonic Audio
Identification of Melodic Notes
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Time (sec)
MIDInotenumber
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Time (sec)
MIDInotenumber
Selection of the Most
Salient Notes
Melody Smoothing
Time
MIDInotenumber
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R1
a2
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a1
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a3

15. 15 Melody Detection in Polyphonic Audio
Identification of Melodic Notes
Elimination of Spurious Accompaniment
Notes
- 1) Delete deep valleys in the salience contour
- 2) Delete isolated abrupt duration transitions
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Note Sequence
AveragePitchSalience

16. 16 Melody Detection in Polyphonic Audio
Identification of Melodic Notes
Elimination of Spurious Accompaniment
Notes
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Time (s)
PitchSalience

17. 17 Melody Detection in Polyphonic Audio
Identification of Melodic Notes
Note Clustering
• Feature Extraction
- Spectral shape, harmonicity, attack transient,
intensity, pitch-related features
• Feature Selection and
Dimensionality Reduction
- Forward feature selection
- Principal Component Analysis
• Clustering
- Gaussian Mixture Models
– 2 clusters: melodic and “garbage” cluster

18. 18 Melody Detection in Polyphonic Audio
Evaluation Procedures
 Ground Truth Data
ID Song Title Category Solo Type
1 Pachelbel’s “Kanon” Classical Instrumental
2 Handel’s “Hallelujah” Choral Vocal
3 Enya – “Only Time” New Age Vocal
4 Dido – “Thank You” Pop Vocal
5 Ricky Martin – “Private Emotion” Pop Vocal
6 Avril Lavigne – “Complicated” Pop/Rock Vocal
7 Claudio Roditi – “Rua Dona Margarida” Jazz/Easy Instrumental
8 Mambo Kings – “Bella Maria de Mi Alma” Bolero Instrumental
9 Eliades Ochoa – “Chan Chan” Son Vocal
10 Juan Luis Guerra – “Palomita Blanca” Bachata Vocal
11 Battlefield Band – “Snow on the Hills” Scottish Folk Instrumental
12 daisy2 Pop Vocal
13 daisy3 Pop Vocal
14 jazz2 Jazz Instrumental
15 jazz3 Jazz Instrumental
16 midi1 Pop Instrumental
17 midi2 Folk Instrumental
18 opera female 2 Opera Vocal
19 opera male 3 Opera Vocal
20 pop1 Pop Vocal
21 pop4 Pop Vocal

19. 19 Melody Detection in Polyphonic Audio
Evaluation Procedures
Evaluation Metrics
• Pitch Contour Accuracy
- Melodic Raw Pitch Accuracy (MRPA)
- Overall Raw Pitch Accuracy (ORPA)
- Melodic Raw Note Accuracy (MRNA)
- Overall Raw Pitch Accuracy (ORNA)
• Note Extraction Accuracy
- Percentage of correct frames
• Melody/Accompaniment Discrimination
- Recall
- Precison

20. 20 Melody Detection in Polyphonic Audio
Overall Results
 Multi-Pitch Detection
• MRPA = 81.0%
 Note Determination
• Frequency-Based Track Segmentation: Re = 72%; Pr = 94.7%
• Salience-Based Track Segmentation: Re = 75%; Pr = 41.2%
 Identification of Melodic Notes
• Elimination of Ghost Notes (EGN)
- Eliminated 37.8% of notes (only 0.3% melodic)
• Overall Melody Extraction
- MRNA = 84.4%; ORNA = 77.0%
• Melody/Accompaniment Discrimination
- Recall = 31.0%; Precision = 52.8%
• MIREX’2004
- Train (note accuracy) = 80.1/ 77.1%; Test = 77.4 / 75.1%
- Train (pitch contour accuracy) = 75.1; 71.1%
• MIREX’2005
- 61.1% overall raw pitch accuracy

21. 21 Melody Detection in Polyphonic Audio
Conclusions and Future Work
 Main Contributions
• Note determination
• Identification of melodic notes in a mixture
 Conclusions
• Adequate MPD approach in a melody context
• Note determination with satisfactory accuracy
• Identification of melodic notes successful in medium
SNR conditions
 Future Work
• Larger musical corpus
• Reduce computational time for pitch detection
• Melody/Accompaniment discrimination