Presentation in 2nd CompMusic workshop in Istanbul, Turkey (2012).

1. Tonic Identification System for
Hindustani and Carnatic Music
Sankalp Gulati, Justin Salamon and Xavier Serra
Music Technology Group
Universitat Pompeu Fabra
{sankalp.gulati, justin.salamon, xavier.serra}@upf.edu

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Introduction: Tonic in Indian art music
P
i
t Tonic
c
h
time
¤ The base pitch chosen by a performer that allows to
explore the full pitch range in a comfortable way [1]
¤ Anchored as ‘Sa’ swar in a performance (mostly)
¤ All the other notes used in the raga exposition derive their
meaning in relation to this pitch value
¤ All other accompanying instruments are tuned using this
pitch as reference
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Role of Drone Instrument
¤ Performer and audience needs to hear this pitch
throughout the concert
¤ Reinforces the tonic and establishes all harmonic and
melodic relationships
Surpeti or
Shrutibox
Sitar Tanpura
Electronic
Tanpura
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Introduction: Tonal structure of Tanpura
¤ Four strings
¤ Tunings
¤ Sa-Sa’-Sa’-Pa
¤ Sa-Sa’-Sa’-Ma
¤ Sa-Sa’-Sa’-Ni
¤ Special bridge with thread
inserted (Jvari)
¤ Violate Helmholtz law [2]
¤ Rich overtones [1]
Bridge
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Introduction: Goals and Motivation
¤ Automatic labeling of the tonic in large databases of
Indian art music
¤ Devise a system for identification of
¤ Tonic pitch for vocal excerpts
¤ Tonic pitch class profile for instrumental excerpts
¤ Use all the available data (audio + metadata) to
achieve maximum accuracy
¤ Confidence measure for each output from the system
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Introduction: Goals and Motivation
¤ Fundamental information
¤ Tonic identification: crucial input for:
¤ Intonation analysis
¤ Raga recognition
¤ Melodic motivic analysis
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Relevant work: Tonic Identification
¤ Very little work done in the past
¤ Based on melody [ 4,5]
¤ Ranjani et al. take advantage of melodic characteristics
of Carnatic music [4]
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Relevant work: Summary
¤ Utilized only the melodic aspects
¤ Used monophonic pitch trackers for heterophonic data
¤ Limited diversity in database
¤ Special raga categories, aalap sections, solo vocal
recordings
¤ Unexplored aspects:
¤ Utilizing background audio content comprising drone sound
¤ Taking advantage of different types of available data, like
audio and metadata
¤ Evaluation on diverse database
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Methodology: System Overview
Manual
annotation
Yes
Audio Metadata
No
No
Yes
Tonic
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Methodology: System Overview
¤ Culture specific characteristics for tonic identification
¤ Presence of drone*
¤ Culture specific melodic characteristics
¤ Raga knowledge
¤ Melodic Motifs
¤ Use variable amount of data that is sufficient enough to
identify tonic with maximum confidence.
¤ Audio data
¤ Metadata (Male/Female, Hindustani/Carnatic, Raga etc.)
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Methodology: Tonic Identification
¤ Audio example:
¤ Utilizing drone sound
¤ Chroma or multi-pitch analysis
Multi-pitch Analysis [7]
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Tonic Identification: Signal Processing
Audio
Sinusoid Extraction
Sinusoids
Pitch Salience
computation
Time frequency salience
Tonic candidate
generation
Tonic candidates
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Tonic Identification: Signal Processing
¤ STFT
¤ Hop size: 11 ms
¤ Window length: 46 ms
¤ Window type: hamming
¤ FFT = 8192 points
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Tonic Identification: Signal Processing
¤ Spectral peak picking
¤ Absolute threshold: -60 dB
¤ Relative threshold: -40 dB
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Tonic Identification: Signal Processing
¤ Frequency/Amplitude
correction
¤ Parabolic interpolation
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Tonic Identification: Signal Processing
¤ Harmonic summation [7]
¤ Spectrum considered: 55-7200 Hz
¤ Frequency range: 55-1760 Hz
¤ Base frequency: 55 Hz
¤ Bin resolution: 10 cents per bin (120 per
octave)
¤ N octaves: 5
¤ Maximum harmonics: 20
¤ Alpha: 1
¤ Beta: 0.8
¤ Square cosine window across 50 cents
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Tonic Identification: Signal Processing
¤ Tonic candidate generation
¤ Number of salience peaks
per frame: 5
¤ Frequency range: 110-550
Hz
¤ After candidate selection
salience is no longer
considered!!!!
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Tonic Identification : Two sub-tasks
¤ Caters to both vocal and instrumental excerpts
¤ Identify tonic pitch class (PC) using multi-pitch histogram
¤ Estimate the correct octave using predominant melody
¤ Use predominant melody extraction approach proposed
by Justin Salamon et al. [6]
¤ Tonic PCP
¤ Peak Picking + Machine learning
¤ Tonic octave estimation
¤ Rule based method + Classification based approach
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Tonic Identification : PC identification
¤ Classification based template learning
¤ Two kind of class mappings
¤ Rank of the highest tonic PC
¤ Highest peak as Tonic or Non tonic
¤ Feature extracted # 20 (f1-f10, a1-a10)
Multipitch Histogram
f2
1
0.9
Normalized salience
0.8
0.7 f3
0.6
f4
0.5 f5
0.4
0.3
0.2
0.1
100 150 200 250 300 350 400
Frequency bins (1 bin = 10 cents), Ref: 55Hz
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Tonic Identification : PC identification
¤ Decision Tree:
Sa
Sa
<=5 >5
salience
Pa
<=-7 >-7 Frequency
Pa
<=5 >5 <=-6 >-6 Sa
Sa
salience
<=-11 >-11
Frequency
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Tonic Identification : Octave Identification
¤ Tonic octave
¤ Rule based method
¤ Classification based approach
¤ 25 Features: a1-a25
Perdominent Melody Histogram
1
Normalized Salience
0.8
0.6
0.4
0.2
0
50 100 150 200 250 300 350
Frequency bins (1 bin = 10 cents), Ref: 55 Hz
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Evaluation: Database
¤ Subset of CompMusic database (>300 Cds) [3]
Approach 2: #540, 3min (PCP) + 238, full recordings (Octave)
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Evaluation: Database
¤ Tonic distribution
60
Female singers
50 Male singers
Number of instances
40
30
20
10
0
120 140 160 180 200 220 240 260 280
Frequency (Hz)
¤ Statistics (for 364 vocal excerpts)
¤ Male (80 %), Female (20%), Hindustani (38%), Carnatic (62%),
Unique artist (#36)
¤ Statistics (for 540 vocal and instrumental excerpts)
¤ Hindustani (36%), Carnatic (64%), Unique artist (#55)
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Evaluation: Annotations
¤ Annotations done by the author
¤ Extracted 5 tonic candidates from multi-pitch histograms
between 110-370 Hz
¤ Matlab GUI to speed up the annotation procedure
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Evaluation: Accuracy measures
¤ Output correct within 50 cents of the ground truth
¤ 10 fold cross validation + rule based classification
¤ Weka: data mining tool
¤ Feature selection: CfsSubsetEval (features > 80% folds)
¤ Classifier: J48 decision tree
¤ Performs better than
¤ SVM-polynomial kernel (6% difference in accuracy)
¤ K* classifier (5% difference in accuracy)
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Results
Class
Approach(%) Map #folds # Features Tonic pitch Tonic PCP 5th 4th Other
EQ
AP1_EXP1 - - - - - 85 10.7 0.93 3.3
AP1_EXP2 M1 1 no 1, S2 - 93.7 1.48 8.9 0.9
AP1_EXP3 M1 10 no 4, S3 - 92.9 1.9 3.5 1.7
AP1_EXP4 M1 10 yes 4, S4 - 74.2 11 7.6 6.7
AP1_EXP5 M2 1 no 1, S2 - 91 3.3 3 2.7
AP1_EXP6 M2 10 no 2, S5 - 91.8 2.2 3 3
AP1_EXP7 M2 10 yes 2, S5 - 87.8 4.2 4 3.9
¤ M1 : tonic PCP rank, M2 : highest peak tonic or non-tonic
¤ S1: [f2, f3, f5], S2: [f2], S3: [f2, f4, f6, a5], S4: [f2, f3, a3, a5], S5: [f2, f3]
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Results
¤ Approach 2, Octave identification
¤ Rule based approach – 99 %
¤ Classification based approach – 100%
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Discussion: PCP Identification
¤ AP-1: Performance for male singers (95%), female singers
(88%)
¤ Error cases
¤ Mostly Ma tuning songs
¤ More female singers
¤ Sensitive to selected frequency range for tonic candidates,
a range of 110-370 Hz works optimal
Pa
Sa
Sa Sa
Sa
Sa Sa
Ma
salience
salience
salience
Pa
Frequency Frequency Frequency
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Discussion : Octave Identification
¤ Challenges faced by rule based approach
¤ Hindustani musicians go roughly -500 cents below tonic
¤ Carnatic musicians generally don’t go that below tonic
¤ Melody estimation errors at low frequency
¤ Concept of Madhyam shruti
Perdominent Melody Histogram
1
Normalized Salience
0.8
0.6
0.4
0.2
0
50 100 150 200 250 300 350
Frequency bins (1 bin = 10 cents), Ref: 55 Hz
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Conclusions and Future Work
¤ Drone sound in the background provides an important
cue for the identification of tonic and can be utilized to
automatically perform this task
¤ System should be fed with more information to
differentiate between ‘Pa’ and ‘Ma’ tuning
¤ Future Work:
¤ Exploring melodic characteristics for tonic identification
¤ Deeper analysis of confidence measure concept
¤ Study influence of cultural background on human
performance for this task
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REFERENCES
1. B. C. Deva. The Music of India: A Scientific Study. Munshiram Manoharlal
Publishers, Delhi, 1980.
2. C. V. Raman. On some Indian stringed instruments. In Indian Association for the
Cultivation of Science, volume 33, pages 29-33, 1921.
3. X. Serra. A multicultural approach in music information research. In 12th Int. Soc. for
Music Info. Retrieval Conf., Miami, USA, Oct. 2011.
4. R. Sengupta, N. Dey, D. Nag, A. K. Datta, and A. Mukerjee, “Automatic Tonic ( SA )
Detection Algorithm in Indian Classical Vocal Music,” in National Symposium on
Acoustics, 2005, pp. 1-5.
5. T. V. Ranjani, H.G.; Arthi, S.; Sreenivas, “Carnatic music analysis: Shadja, swara
identification and rAga verification in AlApana using stochastic models,” Applications
of Signal Processing to Audio and Acoustics (WASPAA), IEEE Workshop, pp. 29-32,
2011.
6. J. Salamon and E. G´omez. Melody extraction from polyphonic music signals using
pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language
Processing, 20(6):1759–1770, Aug. 2012.
7. J. Salamon, E. G´omez, and J. Bonada. Sinusoid extraction and salience function
design for predominant melody estimation. In Proc. 14th Int. Conf. on Digital Audio
Effects (DAFX-11), pages 73–80, Paris, France, Sep. 2011.
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Thank you
Questions?
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