Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis
To enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distingu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10857296/ |
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| author | Zefei Guo Xiao Li Yuchen Zhang |
| author_facet | Zefei Guo Xiao Li Yuchen Zhang |
| author_sort | Zefei Guo |
| collection | DOAJ |
| description | To enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distinguishing itself from previous approaches through its real-time adaptive filtering, which makes it highly adaptable to complex, non-stationary noise conditions. The proposed method is grounded in dynamic multi-scale analysis, employing multi-scale decomposition of speech signals to optimize their time-frequency characteristics and dynamic adjustments, thereby forming a new noise reduction approach, DMS. Initially, the multi-scale decomposition technique effectively captures the multi-scale features of noisy speech signals. Subsequently, optimizing the time-frequency characteristics and dynamic signal adjustments effectively removes noise while improving the signal’s time-frequency resolution. Finally, the method is further enhanced through the adaptive error minimization algorithm, leading to a more pronounced noise reduction effect. Experimental results demonstrate that the proposed method outperforms the single dynamic multi-scale technique in terms of improving signal-to-noise ratio (SNR). |
| format | Article |
| id | doaj-art-736e81c6975740238bde8ab59dcb9274 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-736e81c6975740238bde8ab59dcb92742025-02-07T00:00:56ZengIEEEIEEE Access2169-35362025-01-0113229462295510.1109/ACCESS.2025.353595010857296Optimal Speech Signal Processing Based on Dynamic Multi-Scale AnalysisZefei Guo0https://orcid.org/0009-0008-7824-2495Xiao Li1Yuchen Zhang2https://orcid.org/0009-0009-3907-6946College of Information Science and Technology, Tibet University, Lhasa, Tibet, ChinaCollege of Information Science and Technology, Tibet University, Lhasa, Tibet, ChinaCollege of Information Science and Technology, Tibet University, Lhasa, Tibet, ChinaTo enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distinguishing itself from previous approaches through its real-time adaptive filtering, which makes it highly adaptable to complex, non-stationary noise conditions. The proposed method is grounded in dynamic multi-scale analysis, employing multi-scale decomposition of speech signals to optimize their time-frequency characteristics and dynamic adjustments, thereby forming a new noise reduction approach, DMS. Initially, the multi-scale decomposition technique effectively captures the multi-scale features of noisy speech signals. Subsequently, optimizing the time-frequency characteristics and dynamic signal adjustments effectively removes noise while improving the signal’s time-frequency resolution. Finally, the method is further enhanced through the adaptive error minimization algorithm, leading to a more pronounced noise reduction effect. Experimental results demonstrate that the proposed method outperforms the single dynamic multi-scale technique in terms of improving signal-to-noise ratio (SNR).https://ieeexplore.ieee.org/document/10857296/Adaptive errormulti-scale analysisoptimal estimationspeech noise reductiontime-frequency characteristic optimization |
| spellingShingle | Zefei Guo Xiao Li Yuchen Zhang Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis IEEE Access Adaptive error multi-scale analysis optimal estimation speech noise reduction time-frequency characteristic optimization |
| title | Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis |
| title_full | Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis |
| title_fullStr | Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis |
| title_full_unstemmed | Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis |
| title_short | Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis |
| title_sort | optimal speech signal processing based on dynamic multi scale analysis |
| topic | Adaptive error multi-scale analysis optimal estimation speech noise reduction time-frequency characteristic optimization |
| url | https://ieeexplore.ieee.org/document/10857296/ |
| work_keys_str_mv | AT zefeiguo optimalspeechsignalprocessingbasedondynamicmultiscaleanalysis AT xiaoli optimalspeechsignalprocessingbasedondynamicmultiscaleanalysis AT yuchenzhang optimalspeechsignalprocessingbasedondynamicmultiscaleanalysis |