This document summarizes a study that investigated listener preferences for different multichannel microphone techniques when recording solo piano music. The study found that listener preferences were influenced by both the microphone technique used and the musical selection. Key perceptual attributes like width, sharpness, and bass tightness accounted for preference ratings. Physical measures correlated with these attributes, validating that preferences were grounded in the reproduced sound's physical characteristics. The study provides insights into how multichannel recording techniques can be optimized to create preferred auditory scenes for solo piano music.

1. Recording piano in surround:
discovering preferences,
investigating auditory imagery,
and establishing physical predictors
Kim, Sungyoung

2. Tonmeisters
Need not play a musical instrument
Mainly, they play “microphone(s)”
1 channel: monophonic
2 channel: stereophonic
More than 2 channels: multichannel
(or surround)
2

3. Multichannel Recording Technique
Use of an array of multiple microphones
designed to capture an acoustical event or
performance in a hall, with important
variationin the microphones’ directional
sensitivity and
the distance between them. DIFFERENCES
PHYSICAL
In order to achieve a pleasing sound field…
What perceptual factors are important?
3

4. That question provided the
motivation for this series of
related experiments…
Goal: To obtain a complete representation
of physical, perceptual, and affective
phenomena, capturing the essential
characteristics of reproduced auditory
scenes associated with multichannel
microphone techniques for solo piano music
4

5. Stimuli
Recordings made using 4 different multichannel
microphone arrays;
 Fukada tree inspired
 Polyhymnia pentagon
 OCT + Hamasaki Square
 SoundField + SP451
Piano : New York Steinway model Concert D
in Pollack Hall
5

6. Fukada Tree
• 5 cardioid capsules
• Less limitation on microphone spacing and
angle (supposed to be optimized
according to recording situation)
• Good for large ensembles
• Good envelopment?

7. • Fukada tree inspired : 5 cardioid microphones - DPA 4011

9. Polyhymnia Pentagon
• 5 omni capsules
• Inherited from the legendary Decca Tree
• Good low frequency response (omni)
• Precise image?

10. • Polyhymnia pentagon

12. OCT + Hamasaki Square
• Optimized Cardioid Triangle
• Tuned to optimize “critical link” (less
crosstalk) by G. Theile
• Hamasaki Square is for connection
between front and rear (good
envelopment)
• Valid for any musical instrument?

13. • OCT + Hamasaki Square

15. SoundField Microphone
• 1st order Ambisonic
• Vienna Decoder for 5.1 reproduction

16. • SoundField + SP451

17. Polyhymnia
Pentagon
Fukada
Tree
OCT + Soundfield
Hamasaki Microphone
17

18. MUSICAL SELECTIONS
Four compositions for solo piano judged to be representative of
different stylistic periods were selected by the pianist, Professor
Thomas Plaunt:
 Late Baroque: Johann Sebastian Bach,
“Variation 13”, Goldberg Variations (BWV 988, circa 1741)
 Early Romantic: Franz Peter Schubert,
“1. Allegro ma non troppo”, Sonata in A minor (D537, 1817)
 Romantic: Johannes Brahms,
Ballade in D minor (op. 10 no. 1, 1854)
 Contemporary Improvisation: Thomas C. Plaunt,
untitled (2006)
18

19. Stimuli were reproduced via 5 loudspeakers in
MARLAB (Multichannel Audio Research LAB)
Which reproduction will listeners prefer?
19

20. Obtained preference scale
Fukada
F
Tree
Polyhymnia
P
Pentagon
Obtained preferences
are modulated by
musical selections!
How can we quantitatively predict such observed preferences? 20

21. Investigating auditory imagery
Analysis of Analysis of
binaural recordings perceptual
descriptions
21

22. 1) Via instrumental measurements
How the stimuli are physically different when they
reach to this listener’s two ear, in terms of …
ESI ∙ Similarity between left and right ear signal
∙ Loudness
∙ Spectral distribution
∙ Envelopment
SBR ∙ Ratio between bass and
mid-high portion
∙ etc.
22

23. Predicted preference via ESI & SBR
Whereas ESI & SBR accounted for overall preference,
they were less relevant to perceptual attribute(s).
23

24. 2) Via perceptual measurements
 Identifying relevant
and salient attributes
 Quantification
(ratings)
24

25. Identification
Based on most frequently used descriptors
(by the panel of listeners), five pairs of
bipolar adjectives were defined
Wide ↔ Narrow
Sharp ↔ Dull
Focused ↔ Diffused
Tight-Bass ↔ Muddy-bass
Distant ↔ Close
Then, the same listeners rated the stimuli on these. 25

26. Definition
• Wide/Narrow: The apparent horizontal spatial extent of
the sound source (piano).
• Sharp/Dull: The variation in tone coloration associated
with an increase in high-frequency content relative to low-
frequency content in the sound source (piano).
• Focused/Diffused: The apparent integration
of the sound source (piano) into a single unified image.
• Tight-Bass/Muddy-Bass: The apparent integration of
the low-frequency content in the sound source (piano)
into a single unified image.
• Distant/Close: The apparent spatial distance
of the sound source (piano) from the listening position.
26

27. Analysis of obtained ratings
 Stepwise regression analysis showed that only three of the five
sets of attribute ratings were required to successfully predict
preference ratings
Sharpness
Observed
Width accounted for
Preference
Bass Tightness
What physical measures correspond
with these salient attributes?
27

28. Physical measures of perceptual attributes
 Proposition: Salient attributes
are grounded in physical
characteristics of reproduced
musical sound.
 Physical measures can
validate the obtained ratings
 Ultimately, they provide a
foundation of perceptually
meaningful and systemic
evaluation for a sound field
28

29. 1) Width prediction
Via analysis of ear
signals: binaural
recordings of
multichannel
reproduced piano
music
Sum of differences
between left and
right ear signals
calculated within
each Critical-Band
29

30. 2) Bass tightness prediction
Sum of
differences
between left and
right ear signals
But only at low
frequency region
(up to the 9th
Critical Band:
400Hz CF)
30

31. 3) Sharpness prediction
 Prediction equations based upon conventional
(Zwicker) Sharpness models were not effective
for the obtained sharpness ratings
 When the sharpness ratings were predicted only
for a single musical selection, however, the new
model gave significantly high correlation
between the predicted and rated sharpness
31

32. Iso-Preference Contours
Related to Principal Components
High
Pref.
Low
Pref. R2=0.78
32

33. Iso-Preference Contours
Related to Predicted Attributes
Low
Predicted attributes accounted for overall preference
High
Pref.
in a same way that attribute ratings did Pref.
R2=0.71

34. Conclusion
 Listeners’ preference is modulated by multichannel microphone
techniques and musical selections
 This preference is accounted for by:
1. the degree of similarity between left and right ear signal (ESI)
2. the ratio of bass and mid-high spectrum incoming from side
direction (SBR)
 A sound field captured and reproduced in multiple channels was
characterized by five perceptual attributes; width, sharpness, bass-
tightness, focus, and distance
 Among five attributes, width, sharpness, and bass-tightness
accounted for the variation in preference
 Attributes were validly associated with physical measurements
34

35. sungyoungk@gmail.com
35