Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation
In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cramér-Rao bounds f...
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| Veröffentlicht in: | IEEE transactions on audio, speech, and language processing speech, and language processing, 2013-05, Vol.21 (5), p.923-933 |
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| container_title | IEEE transactions on audio, speech, and language processing |
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| creator | Jensen, J. R. Christensen, M. G. Jensen, S. H. |
| description | In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cramér-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian. |
| doi_str_mv | 10.1109/TASL.2013.2239290 |
| format | Article |
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Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.</description><identifier>ISSN: 1558-7916</identifier><identifier>EISSN: 1558-7924</identifier><identifier>DOI: 10.1109/TASL.2013.2239290</identifier><identifier>CODEN: ITASD8</identifier><language>eng</language><publisher>Piscataway, NJ: IEEE</publisher><subject>Applied sciences ; Cramér-Rao lower bound ; Detection, estimation, filtering, equalization, prediction ; Direction of arrival estimation ; Estimation ; Exact sciences and technology ; Frequency estimation ; fundamental frequency estimation ; Information, signal and communications theory ; joint estimation ; Joints ; non-linear least squares ; Sensors ; Signal and communications theory ; Signal, noise ; Speech ; Speech processing ; Telecommunications and information theory</subject><ispartof>IEEE transactions on audio, speech, and language processing, 2013-05, Vol.21 (5), p.923-933</ispartof><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-93d2334dfc063533b0d994a84c5c9f35bccd523fcb8e7d9b7c8542930c5f1f503</citedby><cites>FETCH-LOGICAL-c338t-93d2334dfc063533b0d994a84c5c9f35bccd523fcb8e7d9b7c8542930c5f1f503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6409418$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6409418$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27450664$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Jensen, J. R.</creatorcontrib><creatorcontrib>Christensen, M. G.</creatorcontrib><creatorcontrib>Jensen, S. H.</creatorcontrib><title>Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation</title><title>IEEE transactions on audio, speech, and language processing</title><addtitle>TASL</addtitle><description>In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cramér-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.</description><subject>Applied sciences</subject><subject>Cramér-Rao lower bound</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Direction of arrival estimation</subject><subject>Estimation</subject><subject>Exact sciences and technology</subject><subject>Frequency estimation</subject><subject>fundamental frequency estimation</subject><subject>Information, signal and communications theory</subject><subject>joint estimation</subject><subject>Joints</subject><subject>non-linear least squares</subject><subject>Sensors</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Speech</subject><subject>Speech processing</subject><subject>Telecommunications and information theory</subject><issn>1558-7916</issn><issn>1558-7924</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwAYiNNywTbI-dxMtSykuBIrWsI8cP1SgkxTYL_p5WrbKaK809o9FB6JqSnFIi79azVZ0zQiFnDCST5ARNqBBVVkrGT8dMi3N0EeMXIRwKTifo_n3oO99bFXBtVUx49fOrgo34zabNYCJ2Q8Cvg-8TfljOsOoN_vBJb_AiJv-tkh_6S3TmVBft1XFO0efjYj1_zurl08t8VmcaoEqZBMMAuHGaFCAAWmKk5KriWmjpQLRaG8HA6baypZFtqSvBmQSihaNOEJgierirwxBjsK7Zht0L4a-hpNlLaPYSmr2E5ihhx9wemK2KWnUuqF77OIKs5IIUBd_1bg49b60d1wUnktMK_gFmBmSA</recordid><startdate>20130501</startdate><enddate>20130501</enddate><creator>Jensen, J. R.</creator><creator>Christensen, M. G.</creator><creator>Jensen, S. H.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20130501</creationdate><title>Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation</title><author>Jensen, J. R. ; Christensen, M. G. ; Jensen, S. 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R.</creatorcontrib><creatorcontrib>Christensen, M. G.</creatorcontrib><creatorcontrib>Jensen, S. H.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>IEEE transactions on audio, speech, and language processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jensen, J. R.</au><au>Christensen, M. G.</au><au>Jensen, S. H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation</atitle><jtitle>IEEE transactions on audio, speech, and language processing</jtitle><stitle>TASL</stitle><date>2013-05-01</date><risdate>2013</risdate><volume>21</volume><issue>5</issue><spage>923</spage><epage>933</epage><pages>923-933</pages><issn>1558-7916</issn><eissn>1558-7924</eissn><coden>ITASD8</coden><abstract>In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cramér-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TASL.2013.2239290</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Applied sciences Cramér-Rao lower bound Detection, estimation, filtering, equalization, prediction Direction of arrival estimation Estimation Exact sciences and technology Frequency estimation fundamental frequency estimation Information, signal and communications theory joint estimation Joints non-linear least squares Sensors Signal and communications theory Signal, noise Speech Speech processing Telecommunications and information theory |
| title | Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation |
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