NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation
Abstract: The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive filtering algorithms. In this paper, we integrate the frequency domain Kalman filter (FDKF) and deep neural networks (DNNs) into a hybrid method, called NeuralKalman, to leverage the advantages of deep learning and adaptive filtering algorithms. Specifically, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed NeuralKalman improves the performance of FDKF significantly and outperforms strong baseline methods.
- Donald L Duttweiler, “Proportionate normalized least-mean-squares adaptation in echo cancelers,” IEEE Transactions on Speech and Audio Processing, vol. 8, pp. 508–518, 2000.
- Steven L Gay, “The fast affine projection algorithm,” in Acoustic Signal processing for Telecommunication, pp. 23–45. 2000.
- “Step-size control for acoustic echo cancellation filters–an overview,” Signal Processing, vol. 80, pp. 1697–1719, 2000.
- “State-space frequency-domain adaptive filtering for nonlinear acoustic echo cancellation,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, pp. 2065–2079, 2012.
- “An efficient RLS algorithm for output-error adaptive IIR filtering and its application to acoustic echo cancellation,” in IEEE Symposium on Computational Intelligence in Image and Signal Processing, 2007, pp. 139–145.
- “Deep learning for acoustic echo cancellation in noisy and double-talk scenarios,” in Proc. Interspeech, 2018, p. 322.
- “Neural cascade architecture for multi-channel acoustic echo suppression,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 2326–2336, 2022.
- “NeuralEcho: A self-attentive recurrent neural network for unified acoustic echo suppression and speech enhancement,” arXiv preprint arXiv:2205.10401, 2022.
- “ICASSP 2022 acoustic echo cancellation challenge,” in Proc. ICASSP, 2022, pp. 9107–9111.
- “Low-complexity, real-time joint neural echo control and speech enhancement based on percepnet,” in Proc. ICASSP, 2021, pp. 7133–7137.
- “Weighted recursive least square filter and neural network based residual echo suppression for the AEC-challenge,” in Proc. ICASSP, 2021, pp. 141–145.
- “Acoustic echo cancellation using deep complex neural network with nonlinear magnitude compression and phase information.,” in Proc. Interspeech, 2021, pp. 4768–4772.
- “A synergistic Kalman and deep postfiltering approach to acoustic echo cancellation,” in Proc. EUSIPCO, 2021, pp. 990–994.
- “Deep adaptive AEC: Hybrid of deep learning and adaptive acoustic echo cancellation,” in Proc. ICASSP, 2022, pp. 756–760.
- “Frequency-domain adaptive Kalman filter with fast recovery of abrupt echo-path changes,” IEEE Signal Processing Letters, vol. 24, pp. 1778–1782, 2017.
- “Frequency-domain adaptive Kalman filter for acoustic echo control in hands-free telephones,” Signal Processing, vol. 86, pp. 1140–1156, 2006.
- “Neural Kalman filtering,” arXiv preprint arXiv:2102.10021, 2021.
- “Kalmannet: Neural network aided kalman filtering for partially known dynamics,” IEEE Transactions on Signal Processing, vol. 70, pp. 1532–1547, 2022.
- “Long short-term memory kalman filters: Recurrent neural estimators for pose regularization,” in Proc. of the IEEE International Conference on Computer Vision, 2017, pp. 5524–5532.
- “Low-complexity acoustic echo cancellation with neural Kalman filtering,” arXiv preprint arXiv:2207.11388, 2022.
- “Deep filtering: Signal extraction and reconstruction using complex time-frequency filters,” IEEE Signal Processing Letters, vol. 27, pp. 61–65, 2019.
- “SDR–half-baked or well done?,” in Proc. ICASSP, 2019, pp. 626–630.
- “AISHELL-2: Transforming mandarin ASR research into industrial scale,” arXiv:1808.10583, 2018.
- “Image method for efficiently simulating small-room acoustics,” The Journal of the Acoustical Society of America, vol. 65, pp. 943–950, 1979.
- “Perceptual evaluation of speech quality (PESQ) - A new method for speech quality assessment of telephone networks and codecs,” in Proc. ICASSP, 2001, pp. 749–752.
- “Tencent ASR,” in http://ai.qq.com/product/aaiasr.shtml.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.