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Noise reduction algorithm with the soft thresholding based on the Shannon entropy and bone-conduction speech cross-
Sung Dae Na1, Qun Wei2, Ki Woong Seong3
1Department of Medical and Biological Engineering, Graduate School, Kyungpook National University, Daegu, Korea.
Summary
This study introduces a novel speech enhancement algorithm using bone-conduction signals and Shannon entropy for improved noise reduction. The method effectively differentiates speech from noise, enhancing communication in challenging environments.
Area of Science:
- Signal Processing
- Acoustics
Background:
- Conventional speech enhancement struggles to distinguish air-conducted speech from noise.
- Difficulty in differentiating target speech from babble or white noise impacts conventional methods.
Purpose of the Study:
- To develop an advanced algorithm for speech enhancement and noise reduction.
- To leverage bone-conduction signals combined with Shannon entropy and cross-correlation for improved speech detection.
Main Methods:
- Proposed a new algorithm utilizing Shannon entropy and cross-correlation of air- and bone-conduction signals.
- Employed soft thresholding on wavelet packet coefficients of noisy speech.
- Generated thresholds using entropy and cross-correlation in decomposed bands via wavelet packet decomposition.
Main Results:
- Achieved efficient results validated by objective quality measures: PESQ, RMSE, Correlation, and SNR.
- Demonstrated significant noise reduction through MATLAB simulations.
- Confirmed high performance and feasibility through comparative analysis of air- and bone-conduction speech signals and spectra.
Conclusions:
- The proposed method effectively reduces noise by utilizing entropy and cross-correlation with bone-conduction signals.
- Validated feasibility and high performance for applications in communication devices, noisy environments, construction, and military operations.
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