An Adaptive Kalman Speech Enhancement Method Driven by Burst Noise Suppression and Dual-time-scale Perception

CHEN Bo; ZHENG ZeRui; SUN Chao; WANG ZheMing; SHEN Ying

doi:10.11999/JEIT260636

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CHEN Bo, ZHENG ZeRui, SUN Chao, WANG ZheMing, SHEN Ying. An Adaptive Kalman Speech Enhancement Method Driven by Burst Noise Suppression and Dual-time-scale Perception[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260636

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CHEN Bo, ZHENG ZeRui, SUN Chao, WANG ZheMing, SHEN Ying. An Adaptive Kalman Speech Enhancement Method Driven by Burst Noise Suppression and Dual-time-scale Perception[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260636

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An Adaptive Kalman Speech Enhancement Method Driven by Burst Noise Suppression and Dual-time-scale Perception

doi: 10.11999/JEIT260636 cstr: 32379.14.JEIT260636

1.
Zhejiang University of Technology, Hangzhou 310014, China
2.
Zhejiang Provincial Key Laboratory of Intelligent Perception and Control of Complex Systems, Hangzhou 310014, China

Funds: The Key Research and Development Program of Zhejiang Province (2025C01108)

Received Date: 2026-05-18
Accepted Date: 2026-07-29
Rev Recd Date: 2026-07-23

Available Online: 2026-08-08

Abstract

Abstract

Objective Traditional Auto-Regressive (AR) Kalman speech enhancement algorithms face three critical challenges under non-stationary noise: limited adaptability of noise model updates, model mismatch caused by burst noise, and a lack of environment-aware covariance adjustment. These limitations degrade enhancement performance and restrict their use in practical speech communication. An improved adaptive Kalman speech enhancement method is therefore proposed to improve speech quality and intelligibility under complex noise conditions. Methods First, an environmental deviation measure based on the Energy Entropy Ratio (EER) is constructed to quantify the statistical deviation between the current frame and the background environment. A dual-time-scale EER tracking mechanism is then established to capture instantaneous variations and stable background statistics. Their difference is used to generate an adaptive deviation intensity factor for the joint adjustment of the process-noise and observation-noise covariances. Second, a two-stage burst noise discrimination scheme is developed based on the characteristics of burst noise. Frame energy changes and the Spectral Flatness Measure (SFM) are jointly used for preliminary burst noise detection. A Speech Modeling Metric (SMM) based on the linear prediction residual variance ratio is further used to distinguish speech from weak-colored burst noise and prevent contamination of the speech AR model. Results and Discussions Experiments are conducted on the NOIZEUS database. The proposed method outperforms the conventional AR-based Kalman filter and the sensitivity-based Augmented Kalman Filter (AKF) in terms of Short-Time Objective Intelligibility (STOI), Perceptual Evaluation of Speech Quality (PESQ), and Segmental Signal-to-Noise Ratio (SegSNR). It shows improved adaptability and stability under non-stationary and burst noise conditions. Dual-time-scale environmental perception and burst noise discrimination facilitate rapid adaptation to environmental changes and reduce speech distortion. Conclusions The proposed burst noise suppression and dual-time-scale adaptive Kalman method addresses the limitations of conventional AR-based Kalman speech enhancement under complex noise conditions. EER-based tracking enables environment-aware covariance adjustment, whereas the two-stage discrimination scheme improves burst noise detection and reduces contamination of the speech AR model. The experimental results demonstrate the robust performance of the proposed method and support its application to speech enhancement under non-stationary noise.
- Adaptive Kalman filtering,
- Speech enhancement,
- Burst noise,
- Energy Entropy Ratio (EER),
- Non-stationary noise

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References(18)

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