reconstruction of mechanically recorded audio …audio signal = evolution of the depth of a...
TRANSCRIPT
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Reconstruction of Mechanically Recorded Audio Signals using White-Light Interferometry
Presenter :Philippe GOURNAY
Authors : Khac Phuc Hung THAIPhilippe GOURNAYSerge CHARLEBOISRoch LEFEBVRE
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A joint project between two research laboratories at the Université de Sherbrooke:
GRPA - Speech and Audio Research Group (image and audio processing)
CRN2 – Nanofabrication and Nanocharacterization Research Center (measuring equipment)
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Reconstruct a digital audio signal from an Edison cylinder using non-contact profilometry
Earlier methods used a confocal probe which scans the recording medium one point at a time (Fadeyev 2005)
We use of a 3D optical profilometerwhich provides topographic information about an entire section in one operation
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OUTLINE
1. Blue Amberol Edison cylinders 2. White-light interferometry for surface
profiling 3. Audio reconstruction using 3D optical
profilometry 4. Experimental results
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1. Blue Amberol cylinders
Vertical cut recording process: audio signal = evolution of the depth of a continuous groove etched at the surface of the cylinder.
Spatial resolution needed to extract audio with Fe=48kHz:◦ Lateral 5µm
◦ Longitudinal 10µm
◦ Vertical 10nm
High resolution: micro-profilometry required
External diameter (mm) 55
Length (mm) 95
Rotation speed (rpm) 160
Groove density (tracks/cm) 79
Groove spacing (μm) 127
Groove depth (μm) 15-30
Audio duration Approx. 4 minutes
Audio bandwidth 150 Hz to 5 kHz
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2. White-light interferometry for surface profiling Principle
FOGALE Photomap 3D: 20x optical magnification chosen for our acquisition
Field of view: approximately 834 µm by 630 µm.
Resolution: at least 1µm laterally and 1nm vertically.
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3. Audio reconstruction using 3D optical profilometry
3.1 • Topographic data acquisition
3.2 • Surface curvature compensation
3.3 • Groove detection and segment extraction
3.4 • Segment concatenation
3.5 • Audio post-processing
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3. Audio reconstruction using 3D optical profilometry
3.1 • Topographic data acquisition
3.2 • Surface curvature compensation
3.3 • Groove detection and segment extraction
3.4 • Segment concatenation
3.5 • Audio post-processing
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3.1. Topographic data acquisition
Each measurement with the 3D profilometer: 1. A conventional (optical) grayscale microscopic image.2. A topographic image, also in grayscale but in which each
intensity corresponds to a certain depth in nm. Field of view : 615 µm by 520 µm; spatial resolution: 1 µm 5x5 median filter Down-sampled for 48 kHz
Audio sampling frequency 20% overlap between
measurements
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3. Audio reconstruction using 3D optical profilometry
3.1 • Topographic data acquisition
3.2 • Surface curvature compensation
3.3 • Groove detection and segment extraction
3.4 • Segment concatenation
3.5 • Audio post-processing
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3.2. Surface curvature compensation
Vertical lines in a topographic image appear at different elevations
Compensation using a simple application of Pythagorean theorem
Average “external” surface elevation before and after compensation:
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3. Audio reconstruction using 3D optical profilometry
3.1 • Topographic data acquisition
3.2 • Surface curvature compensation
3.3 • Groove detection and segment extraction
3.4 • Segment concatenation
3.5 • Audio post-processing
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3.3. Groove detection and segment extraction
Work on vertical lines in profilometric image Local minimum: elevation lower than 8 nearest neighbours Local minima must be at least 127 µm apart Use all vertical lines to build a short segment Some post-processing to reconstitute any missing or clearly
erroneous point.
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3. Audio reconstruction using 3D optical profilometry
3.1 • Topographic data acquisition
3.2 • Surface curvature compensation
3.3 • Groove detection and segment extraction
3.4 • Segment concatenation
3.5 • Audio post-processing
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3.4. Segment concatenation
20% overlap between adjacent measurement Correlate corresponding overlapping extremities to align
short segments Work on derivative signals because of a possible mismatch
in vertical alignment between consecutive measurements
Valley 1
Valley 2
Valley 3
x_offset
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3.4. Segment concatenation
Examples of correlation signals and corresponding overlapping extremities (note the vertical shift between images):
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3. Audio reconstruction using 3D optical profilometry
3.1 • Topographic data acquisition
3.2 • Surface curvature compensation
3.3 • Groove detection and segment extraction
3.4 • Segment concatenation
3.5 • Audio post-processing
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3.5. Audio post-processing
Simple 2-degree Butterworth high-pass filter: Fc = 800 Hz.
Simulate transfer function of a mechanical player + remove LF noise caused by surface deformations
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4. Experimental results
We scanned two turns from two different cylinders
Cylinders were manually turned and carefully repositioned between each measurement
One turn = approximately 350measurements
Allows us to reconstruct 3 complete groove turns, which correspond to 1.125 seconds of audio signal
+ used a vintage mechanical player (Amberola 30) to obtain reference signals
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4. Experimental results
Cylinder 1: Blue Amberol #2664 (My heart at thy sweet voice)
Reconstructed tonal signal
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4. Experimental results
Cylinder 2: Blue Amberol #3124 (With his hands in his pockets)
Reconstructed speech signal
Corresponding reference:
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4. Experimental results
Frequency content:
Temporalrepresentation:
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Conclusion
Good subjective quality of reconstructed audio signal
Acquisition still very long (40 seconds per measurement, 350 measurements for one cylinder turn = 1.125s of audio signal)
Limited audio bandwidth of the reconstructed signal (limited vertical resolution for complex surfaces?)
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Thank you!