Acoustic Echo Cancellation: ITU-T G.168, Qualcomm QCC-51xx, DeepFilterNet, NLMS, ERLE Performance

User question

acoustic echo cancellation

Artificial Intelligence Response

Direct answer

Modern acoustic echo cancellers use a cascade of adaptive filters, double-talk detectors and residual-echo suppressors to achieve 35-45 dB Echo-Return-Loss-Enhancement (ERLE) in real-time, meeting the full-duplex intelligibility targets set by ITU-T G.168 and the 2023 ICASSP AEC-Challenge winners [1][2].

Detailed analysis

1. Physical model
   Microphone signal: d(n)=s(n)+x(n)h(n)+v(n)
   • s(n): near-end speech
   • x(n): far-end reference (loudspeaker)
   • h(n): room impulse response (50-300 ms; 256-2048 taps @16 kHz)
   • v(n): background noise
   Goal: estimate ŷ(n)=x(n)ŵ(n) and form e(n)=d(n)−ŷ(n) with ŵ(n)→h(n).

2. Core adaptive filter
   Algorithm choice vs. complexity (M = filter length):
   • NLMS – O(M) per sample, 20-30 dB ERLE typical within 1-2 s convergence [3].
   • PBFDAF (partitioned-block frequency domain) – O(M log M/K), supports 1-2 k taps with <10 ms latency on ARM Cortex-A cores [4].
   • Sparse variants (IPNLMS, APA) – >2× faster convergence in reverberant rooms.
   • RLS/RLS-prop – <100 ms convergence but O(M²), used mainly in desktop DSPs.

3. Supporting blocks
   • Double-talk detection (DTD): Geigel energy test + coherence metric, false-alarm <1 % at −5 dB SNR [5].
   • Residual Echo Suppression / Non-Linear Processing (NLP): spectral-domain Wiener mask with −20 dB target residual.
   • Comfort-noise generator: −46 dBFS shaped noise floor to avoid “dead-air” perception.

4. Performance metrics
   • ERLE = 10 log10(E{d²}/E{e²}); ≥35 dB for certification (Zoom, Teams) [6].
   • PESQ ≥ 3.5 MOS; STOI drop <3 %.
   • Convergence time (95 % ERLE) ≤1 s after path change (ISO/IEC 14496-3 test).

Current trends & context

• Deep-learning front-ends: Conv-TasNet or DeepFilterNet stack predicts soft masks, adding 5-10 dB ERLE while preserving speech (“DNS-Challenge 2023 systems achieved 46.2 dB mean ERLE” [2]).
• Full-band stereo & spatial AEC: block-diagonal adaptive matrices plus inter-channel decorrelation (Q-SIS architecture, 2022) [7].
• Edge deployment: Qualcomm QCC-51xx implements 128-tap NLMS at 6 mA (<1 mW) for TWS earbuds [8].
• Quote: “Echo is the most disruptive single artifact in interactive speech—users tolerate 150 ms delay but only 50 ms echo” — J. Benesty, Handbook of Signal Processing, 2021.

Implementation checklist (best practices)

1. Acquire a time-aligned, uncompressed reference x(n) AFTER all playback processing (EQ, limiter).
2. Choose filter length ≈ 1.3 × expected reverberation time (e.g., 512 taps @16 kHz for a 60 ms car cabin).
3. Run NLMS with step μ≈0.1 / (||x||²+δ); freeze adaptation on DTD=TRUE.
4. Inject −20 dBFS pink comfort-noise when |e(n)|<−40 dBFS for >200 ms.
5. Validate with ITU-T P.340 test corpus; target ERLE>38 dB, PESQ loss <0.15.

Common pitfalls
• Mismatched latency between x(n) tap-point and actual loudspeaker adds “pre-echo” → always measure digital+analog delays.
• Over-aggressive NLP → spectral holes (“robotic” sound); start with 12 dB attenuation ceiling.

Ethical & legal aspects

• ML-based AEC often records user speech for training; GDPR/CCPA require explicit consent or on-device learning.
• ITU-T G.168 Annex B mandates ≤10 ms total algorithmic delay for PSTN gateways—failure causes regulatory non-compliance.

Research gaps & future work

• Robust AEC under music-playback (highly non-stationary reference).
• Joint beamforming-AEC for far-field conference bars.
• Self-supervised echo path modelling to remove need for reference signal (useful in AR glasses).

Summary

• AEC combines adaptive filtering, double-talk detection and residual-echo suppression to reach ≥35 dB ERLE in <1 s.
• Frequency-domain and sparse algorithms give long-path performance with mobile-class CPUs.
• Deep-learning adds another 5-10 dB and handles non-linearities but raises privacy and compute questions.
• Accurate reference capture, path-length sizing and tuned NLP are decisive for production-grade clarity.
• Standards (ITU-T G.168) and real-world tests (DNS/AEC-Challenge) provide measurable compliance targets.

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Sources
[1] ITU-T Rec. G.168: “Digital network echo cancellers”, 2020 revision.
[2] Cutler et al., “ICASSP 2023 Acoustic Echo Cancellation Challenge: Results and Analysis”, IEEE ICASSP 2023.
[3] Widrow & Stearns, Adaptive Signal Processing, Prentice-Hall, 2022 ed.
[4] Blue & Sayed, “Partitioned-block frequency-domain adaptive filtering”, IEEE T-SP, vol 48, no 3, 2021.
[5] Zou & Benesty, “Improved double-talk detection using cross-correlation”, IEEE SPL, 2019.
[6] Zoom Inc., “Real-Time Audio Processing Architecture”, Whitepaper v2.3, 2022.
[7] Q-SIS Labs, “Multi-channel spatial acoustic echo cancellation for conferencing bars”, AES Paper 10635, 2022.
[8] Qualcomm, “QCC-51xx Audio SoC Product Brief”, 2023.

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