On new efficient μ-law-based method for feedback compensation in hearing aids
The affine-projection-like (APL) algorithm is reported to achieve lower computations than affine-projection algorithm (APA) without compromising the steady-state performance. Further, the performance accuracy of the adaptive feedback canceller (AFC) in hearing aids is enhanced using an improved prop...
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| Veröffentlicht in: | Electronics letters 2016-07, Vol.52 (14), p.1200-1202 |
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| container_title | Electronics letters |
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| creator | Vasundhara Panda, G Puhan, N.B |
| description | The affine-projection-like (APL) algorithm is reported to achieve lower computations than affine-projection algorithm (APA) without compromising the steady-state performance. Further, the performance accuracy of the adaptive feedback canceller (AFC) in hearing aids is enhanced using an improved proportionate APL (IPAPL) algorithm. Two new learning algorithms are proposed for AFC, which apply the memory of previous gain factors and μ-law proportionate technique to the IPAPL, termed as memorised IPAPL (MIPAPL) and μ-law MIPAPL (MMIPAPL), respectively. In addition, a segmented approach is also suggested which offers computational advantage over MMIPAPL. The results obtained from simulation-based experiments demonstrate that the proposed methods achieve faster convergence rate than the existing methods. |
| doi_str_mv | 10.1049/el.2016.0483 |
| format | Article |
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Further, the performance accuracy of the adaptive feedback canceller (AFC) in hearing aids is enhanced using an improved proportionate APL (IPAPL) algorithm. Two new learning algorithms are proposed for AFC, which apply the memory of previous gain factors and μ-law proportionate technique to the IPAPL, termed as memorised IPAPL (MIPAPL) and μ-law MIPAPL (MMIPAPL), respectively. In addition, a segmented approach is also suggested which offers computational advantage over MMIPAPL. 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Further, the performance accuracy of the adaptive feedback canceller (AFC) in hearing aids is enhanced using an improved proportionate APL (IPAPL) algorithm. Two new learning algorithms are proposed for AFC, which apply the memory of previous gain factors and μ-law proportionate technique to the IPAPL, termed as memorised IPAPL (MIPAPL) and μ-law MIPAPL (MMIPAPL), respectively. In addition, a segmented approach is also suggested which offers computational advantage over MMIPAPL. The results obtained from simulation-based experiments demonstrate that the proposed methods achieve faster convergence rate than the existing methods.</description><subject>adaptive feedback canceller</subject><subject>AFC</subject><subject>affine‐projection‐like algorithm</subject><subject>Biomedical technology</subject><subject>feedback</subject><subject>feedback compensation</subject><subject>filtering theory</subject><subject>gain factors</subject><subject>hearing aids</subject><subject>improved proportionate APL</subject><subject>IPAPL</subject><subject>learning (artificial intelligence)</subject><subject>learning algorithms</subject><subject>medical signal processing</subject><subject>memorised IPAPL</subject><subject>MIPAPL</subject><subject>MMIPAPL</subject><subject>μ‐law MIPAPL</subject><issn>0013-5194</issn><issn>1350-911X</issn><issn>1350-911X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp90L1OwzAQB3ALgURVuvEAHhgYSLmLnaQZoWoBqaILSGyWPy7UkDpVHFT13XgGnolUZWBATDfc7053f8bOEcYIsrymepwC5mOQE3HEBigySErEl2M2AECRZFjKUzaK0RtAiTIHiQP2uAw80JZTVXnrKXT86zOp9TYxOpLja-pWjeNV0_KKyBlt37lt1hsKUXe-CdwHviLd-vDKtXfxjJ1Uuo40-qlD9jyfPU3vk8Xy7mF6s0iskCJPjKTCWmmLNMsppcJoMBasKTJJfSul0uYgQIvMaQRtdWoK49CidHoiUxRDdnXYa9smxpYqtWn9Wrc7haD2cSiq1T4OtY-j59mBb31Nu3-tmi0W6e0cRJnn_dzFYc5Tp96ajzb0T_XiF9-4qmeXf7A_L_kGp-t8WA</recordid><startdate>20160707</startdate><enddate>20160707</enddate><creator>Vasundhara</creator><creator>Panda, G</creator><creator>Puhan, N.B</creator><general>The Institution of Engineering and Technology</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20160707</creationdate><title>On new efficient μ-law-based method for feedback compensation in hearing aids</title><author>Vasundhara ; Panda, G ; Puhan, N.B</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3436-b4e7cc4c7256e2e7ba0bc0cb754e4e72e9c6030a35da10aca2b7bd1c14da84213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>adaptive feedback canceller</topic><topic>AFC</topic><topic>affine‐projection‐like algorithm</topic><topic>Biomedical technology</topic><topic>feedback</topic><topic>feedback compensation</topic><topic>filtering theory</topic><topic>gain factors</topic><topic>hearing aids</topic><topic>improved proportionate APL</topic><topic>IPAPL</topic><topic>learning (artificial intelligence)</topic><topic>learning algorithms</topic><topic>medical signal processing</topic><topic>memorised IPAPL</topic><topic>MIPAPL</topic><topic>MMIPAPL</topic><topic>μ‐law MIPAPL</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vasundhara</creatorcontrib><creatorcontrib>Panda, G</creatorcontrib><creatorcontrib>Puhan, N.B</creatorcontrib><collection>CrossRef</collection><jtitle>Electronics letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Vasundhara</au><au>Panda, G</au><au>Puhan, N.B</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On new efficient μ-law-based method for feedback compensation in hearing aids</atitle><jtitle>Electronics letters</jtitle><date>2016-07-07</date><risdate>2016</risdate><volume>52</volume><issue>14</issue><spage>1200</spage><epage>1202</epage><pages>1200-1202</pages><issn>0013-5194</issn><issn>1350-911X</issn><eissn>1350-911X</eissn><abstract>The affine-projection-like (APL) algorithm is reported to achieve lower computations than affine-projection algorithm (APA) without compromising the steady-state performance. Further, the performance accuracy of the adaptive feedback canceller (AFC) in hearing aids is enhanced using an improved proportionate APL (IPAPL) algorithm. Two new learning algorithms are proposed for AFC, which apply the memory of previous gain factors and μ-law proportionate technique to the IPAPL, termed as memorised IPAPL (MIPAPL) and μ-law MIPAPL (MMIPAPL), respectively. In addition, a segmented approach is also suggested which offers computational advantage over MMIPAPL. The results obtained from simulation-based experiments demonstrate that the proposed methods achieve faster convergence rate than the existing methods.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/el.2016.0483</doi><tpages>3</tpages></addata></record> |
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| ispartof | Electronics letters, 2016-07, Vol.52 (14), p.1200-1202 |
| issn | 0013-5194 1350-911X 1350-911X |
| language | eng |
| recordid | cdi_iet_journals_10_1049_el_2016_0483 |
| source | Wiley Online Library Open Access |
| subjects | adaptive feedback canceller AFC affine‐projection‐like algorithm Biomedical technology feedback feedback compensation filtering theory gain factors hearing aids improved proportionate APL IPAPL learning (artificial intelligence) learning algorithms medical signal processing memorised IPAPL MIPAPL MMIPAPL μ‐law MIPAPL |
| title | On new efficient μ-law-based method for feedback compensation in hearing aids |
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