Robust modeling of acoustic paths using a sparse adaptive algorithm

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• 作者：Jyoti Maheshwari ; Nithin V. George ; ^{nithin@iitgn.ac.in" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Sparse model ; System identification ; Robust least mean square algorithm ; Hearing aid ; Acoustic path
• 刊名：Applied Acoustics
• 出版年：2016
• 出版时间：1 January 2016
• 年：2016
• 卷：101
• 期：Complete
• 页码：122-126
• 全文大小：587 K

文摘

Acoustic impulse response functions are generally sparse in nature and traditionally these are modeled by adaptive finite impulse response (FIR) filters trained using a least mean square (LMS) algorithm. The conventional LMS algorithm is not effective in modeling sparse systems and sparse LMS algorithms have been recently developed to improve the modeling in such scenarios. However, the traditional sparse LMS algorithms are not robust to disturbances at the error sensor and may diverge in some scenarios. With an objective to overcome this limitation of conventional sparse adaptive algorithm, this paper presents a robust sparse adaptive algorithm. The new algorithm has been shown to effectively model sparse systems in a robust manner. In addition, the algorithm has been successfully applied in modeling the acoustic feedback path in a behind the ear digital hearing aid.