Robust acoustic echo cancellation using Kalman filter in double talk scenario

• Kalman filtering framework.
• Joint acoustic echo and noise cancellation.
• Double talk detector.
• Linear prediction coding analysis.
• Expectation maximisation algorithm.

In this work, a novel Kalman filtering framework is developed for joint acoustic echo and noise cancellation in a double talk scenario. The efficiency of echo cancellation algorithms is reduced when signals other than the echoed far end signal are present, since the echo path cannot be modelled accurately in such cases. A double talk detector is also used in conjunction with an acoustic echo canceller to handle such a double talk scenario. The method presented in this work is able to model both the near-end speech signal and background noise, which makes lt robust in double talk scenarios. Apart from jointly cancelling echo and noise, another advantage of this framework is that it does not require a double talk detector. Additionally an expectation maximisation based algorithm is also proposed in this work to estimate linear prediction coefficients of the near end signal. Extensive performance evaluation over the NOIZEUS corpus demonstrates that the proposed framework performs reasonably better than other speech enhancement methods in terms of misalignment of the estimated echo path and perceptual quality of the reconstructed near-end speech signal.