Acoustic Echo and Noise Control: A Practical Approach
Acoustic Echo and Noise Control: A Practical Approach
Toeplitz And Circulant Matrices: A Review (Foundations and Trends(R) in Communications and Information Theory)
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Signal processing in high-end hearing aids: state of the art, challenges, and future trends
EURASIP Journal on Applied Signal Processing
Codebook-Based Bayesian Speech Enhancement for Nonstationary Environments
IEEE Transactions on Audio, Speech, and Language Processing
Multicomponent AM–FM Representations: An Asymptotically Exact Approach
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Codebook driven short-term predictor parameter estimation for speech enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Estimation of the short-term predictor parameters of speech under noisy conditions
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Fast reduction of speckle noise in real ultrasound images
Signal Processing
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Conventional single-channel noise reduction algorithms typically have problems with non-stationary noise. Popular algorithms such as minimum statistics or voice-activity-detector-based methods rely on the assumption that the noise spectral characteristics change very slowly over time. Codebook-based approaches try to overcome this problem by incorporating a priori knowledge about speech and different noise types. These approaches perform a joint estimation of the speech and noise spectra on a frame-by-frame basis. The frames are typically 20-40ms long so that fast fluctuations of the signal characteristics can be tracked instantaneously. However, these methods require a pitch estimator to prevent speech distortion as well as residual noise in voiced speech frames. In addition, they are not very robust against model mismatch. In this paper, we propose an integrated noise estimation algorithm that combines the ability of codebook-based algorithms to track non-stationary noise with the robustness of a recursive minimum-tracking-based noise estimation algorithm. An objective and subjective evaluation is provided. Results confirm the superiority of the proposed algorithm in non-stationary noise scenarios compared to state-of-the-art algorithms.