The workshop discussed approaches to error-tolerant audio coding for networked audio. The panelists presented on classification of audio error control strategies including error correction, error limiting, and error concealment. Error correction can be done with independent or dependent source coding. Dependent source coding prioritizes error protection based on perceptual significance. Error limiting aims to limit error propagation through frequent resynchronization points. Error concealment techniques try to reduce the perceptual impact of errors.

1. Error-Tolerant Audio Coding Workshop
Networked Audio Track : Event N1
David Trainor, Director of Advanced Audio Research, CSR
26th October 2012
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2. Workshop Overview
 Some networked audio trends
– Hierarchical broadcast / multicast networks
• Wide-, local- and personal-area
– Real-time / interactive / low-delay audio services e.g. gaming
– Networks with more complex QoS characteristics (e.g. wireless)
• Convenient and inexpensive, but reliability is an issue
 Audio coding is vital (e.g. network bandwidth management)
– Minimally affected by network reliability fluctuations
 This workshop discusses
– Approaches to and capabilities of error-tolerant audio coding
– Recent advances in state-of-the-art
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3. Your Panellists
 Dr David Trainor, CSR (Workshop Chair)
 Dr Gary Spittle, Dolby Labs
 Dr Deepen Sinha, ATC Labs
 Dr Bernhard Grill, Fraunhofer IIS
 Workshop Format
– 25 minute presentation by each panellist
• (20 minutes + 5 minutes Q&A)
– 15-20 minute general Q&A session
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4. Error-Tolerant Audio Coding
General Concepts and Techniques
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5. Classification of Audio Error Control Strategies
 Error correction
– FEC versus ARQ
– Dependent source coding versus independent source coding
– Typically exhibits two levels of success
• Corrects each error event flawlessly or fails completely
 Error limiting
– Limiting of catastrophic propagation of error events
– Sender-based, Receiver-based or Sender-and-Receiver-based
– Dependent source coding versus independent source coding
– Several levels and measures of “success” in perceptual terms
• Error propagation can continue to different degrees, but not
beyond a prescribed limit
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6. Classification of Audio Error Control Strategies (2)
 Error concealment
– Reduce perceptual significance of error events
– Sender-based, Receiver-based or Sender-and-Receiver-based
– Dependent source coding versus independent source coding
– Many levels and measures of “success” in perceptual terms
• PEAQ/PESQ objective testing
• Subjective quality measurements
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7. Error Correction with Independent Source Coding
 Applied at networking baseband or media access layer
– CRC-based detection
– FEC codes
– ARQ retransmissions
 Redundancy not applied in an audio-optimized way
– Packet payload treated as arbitrary data (equal error protection)
 Bit-rate and delay compromises
– Packet-based networks may require additional time-domain
interleaving, etc
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8. Error Correction with Dependent Source Coding
 Coded syntax protection prioritization
– Each field protected according to perceptual significance
– Unequal error protection across coded frame/stream fields
 Scalable coding
– Each coded layer protected according to perceptual significance
– Unequal error protection across coded layers
 These techniques augmented by network QoS prioritization
– Send critical coded frame values or coded audio layers over
network channels with higher QoS parameters
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9. Unequal Error Protection Examples
NON-SCALABLE CODEC
Field
1
Field 2 Field 3 ... Field
N-1
Field N Coded Frame
SCALABLE CODEC
Field
1
Field
2
Field 3 ... Field
N-1
Field N
Mid-Quality Stereo
Base Layer
Field
1
Field 2 Field 3 ... Field N-1
Field High-Quality Stereo
N Enhancement Layer
Field
1
Field
2
Field 3 Field 4 ... Field N-1
Field Parametric Upmixing
N Enhancement Layer
Green = low protection
Orange = medium protection
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Red = high protection
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10. Error Correction with Dependent Source Coding (2)
 Joint quantization and error control code insertion
– Trade small quantization noise increase for improved robustness
 Dynamic data segmentation
– Encode time-varying amounts of audio data
– Choose audio block size based on (for example)
• Communications channel reliability (probability of data loss)
• Network baseband packet size
 Possible compromises of error correction + audio coding
– Bit-rate, delay, codec computational complexity
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11. Error Limiting with Dependent Source Coding
 Key goal is to provide frequent points of resynchronization
– Error event can’t propagate beyond the next synchronization point
 Special synchronization codes/values
– Zero or low probability of occurrence in actual coded audio values
 Error Resilient Entropy Coding
– Insert variable-length entropy codes into fixed-length “slots”
– Partial codes take up spare space at the end of future slots
– Each slot guaranteed to start with first bit of a valid entropy code
– Codes are self-delimiting, hence frequent synchronization points
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12. Error Resilient Entropy Coding Concept
Insert variable length
codes in fixed-length
slots
Slot 1 Slot 2 Slot 3 Slot 4 Slot 5 Slot 6 Slot 7 Slot 8
Partial codes that don’t
fit in their slot are put into
the spare space in later
slots
Start of a code.
Frequent point of synchronization
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13. Error Limiting with Dependent Source Coding (2)
 Reversible Variable Length Codes
– Code sequence readable both forward and backwards in time
– Parsing in both directions can reveal inconsistencies and hence
errors.
 Fixed-length coding techniques
 Limit differential coding techniques
– Restart the differential or progressive coding technique
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14. Error Concealment with Independent Source Coding
 Padding silence or wideband noise
 Pitch estimation and synthesis
– Estimate dominant pitches from previous good frames
– Smooth discontinuities at boundaries
 Replacement with previous best-matching segments
– Simpler forms may be correlation-based
– Sophisticated forms
• SoFi (University of Ulster). MPEG-7 + semantic song analysis.
 Filtered wideband noise generation
– Noise shaped using spectrum of previous good frames
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15. Error Concealment with Independent Source Coding
 Dynamic data segmentation (discussed previously)
 Coded domain interleaving
– Spectral (packet loss only affects specific frequencies)
– Linear Prediction quantized prediction errors
 Linear Predictive Coding specific methods
– Coder parameters
• Copy coder parameters from last good frame
• Coder parameters from last good frame with scaled-down gains
– Stimulate predictor with statistically-shaped synthesized data
(coded residuals)
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