Multisensor Dynamic Waveform Fusion
Speech communication is significantly more difficult in severe acoustic background noise environments, especially when low-rate speech coders are used. Non-acoustic sensors, such as radar sensors, vibrometers, and bone-conduction microphones, offer significant potential in these situations. We exten...
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| creator | McCree, A. Brady, K. Quatieri, T. F. |
| description | Speech communication is significantly more difficult in severe acoustic background noise environments, especially when low-rate speech coders are used. Non-acoustic sensors, such as radar sensors, vibrometers, and bone-conduction microphones, offer significant potential in these situations. We extend previous work on fixed waveform fusion from multiple sensors to an optimal dynamic waveform fusion algorithm that minimizes both additive noise and signal distortion in the estimated speech signal. We show that a minimum mean squared error (MMSE) waveform matching criterion results in a generalized multichannel Wiener filter, and that this filter will simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. Formal intelligibility and quality testing demonstrate significant improvement from this approach. |
| doi_str_mv | 10.1109/ICASSP.2007.366978 |
| format | Conference Proceeding |
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F.</creatorcontrib><title>Multisensor Dynamic Waveform Fusion</title><title>2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07</title><addtitle>ICASSP</addtitle><description>Speech communication is significantly more difficult in severe acoustic background noise environments, especially when low-rate speech coders are used. Non-acoustic sensors, such as radar sensors, vibrometers, and bone-conduction microphones, offer significant potential in these situations. We extend previous work on fixed waveform fusion from multiple sensors to an optimal dynamic waveform fusion algorithm that minimizes both additive noise and signal distortion in the estimated speech signal. We show that a minimum mean squared error (MMSE) waveform matching criterion results in a generalized multichannel Wiener filter, and that this filter will simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. Formal intelligibility and quality testing demonstrate significant improvement from this approach.</description><subject>Acoustic sensors</subject><subject>Background noise</subject><subject>Microphones</subject><subject>Noise cancellation</subject><subject>Non-acoustic sensor</subject><subject>Oral communication</subject><subject>Radar</subject><subject>Sensor fusion</subject><subject>Speech enhancement</subject><subject>Vibrometers</subject><subject>waveform fusion</subject><subject>Wiener filter</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424407279</isbn><isbn>1424407273</isbn><isbn>9781424407286</isbn><isbn>1424407281</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVjs1KAzEURuMfONa-gG4GXGe8N5PkJktprQoVhSq6K4mTQKQzI5NW6Ns7oBs351sc-DiMXSBUiGCvH2Y3q9VzJQCoqrW2ZA7YdCRKISWQMPqQFaImy9HC-9E_R_aYFagEcI3SnrKznD8BwJA0Bbt63G22KYcu90M533euTR_lm_sOsR_acrHLqe_O2Ul0mxymfzthr4vbl9k9Xz7djWFLnpDUlnsbNKronJWNrCN6QUYp9CpSJGVD4wJp14gxCzT6SB6lJ-sRHIFysZ6wy9_fFEJYfw2pdcN-LQUa1Lr-AQpGRD0</recordid><startdate>200704</startdate><enddate>200704</enddate><creator>McCree, A.</creator><creator>Brady, K.</creator><creator>Quatieri, T. F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200704</creationdate><title>Multisensor Dynamic Waveform Fusion</title><author>McCree, A. ; Brady, K. ; Quatieri, T. F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b9e615faa94d43f1b278551b5f7f759edae76ad2424061bf7b14b79b10a705af3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Acoustic sensors</topic><topic>Background noise</topic><topic>Microphones</topic><topic>Noise cancellation</topic><topic>Non-acoustic sensor</topic><topic>Oral communication</topic><topic>Radar</topic><topic>Sensor fusion</topic><topic>Speech enhancement</topic><topic>Vibrometers</topic><topic>waveform fusion</topic><topic>Wiener filter</topic><toplevel>online_resources</toplevel><creatorcontrib>McCree, A.</creatorcontrib><creatorcontrib>Brady, K.</creatorcontrib><creatorcontrib>Quatieri, T. F.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>McCree, A.</au><au>Brady, K.</au><au>Quatieri, T. F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multisensor Dynamic Waveform Fusion</atitle><btitle>2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07</btitle><stitle>ICASSP</stitle><date>2007-04</date><risdate>2007</risdate><volume>4</volume><spage>IV-577</spage><epage>IV-580</epage><pages>IV-577-IV-580</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424407279</isbn><isbn>1424407273</isbn><eisbn>9781424407286</eisbn><eisbn>1424407281</eisbn><abstract>Speech communication is significantly more difficult in severe acoustic background noise environments, especially when low-rate speech coders are used. Non-acoustic sensors, such as radar sensors, vibrometers, and bone-conduction microphones, offer significant potential in these situations. We extend previous work on fixed waveform fusion from multiple sensors to an optimal dynamic waveform fusion algorithm that minimizes both additive noise and signal distortion in the estimated speech signal. We show that a minimum mean squared error (MMSE) waveform matching criterion results in a generalized multichannel Wiener filter, and that this filter will simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. Formal intelligibility and quality testing demonstrate significant improvement from this approach.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2007.366978</doi></addata></record> |
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| identifier | ISSN: 1520-6149 |
| ispartof | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007, Vol.4, p.IV-577-IV-580 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_4218166 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Acoustic sensors Background noise Microphones Noise cancellation Non-acoustic sensor Oral communication Radar Sensor fusion Speech enhancement Vibrometers waveform fusion Wiener filter |
| title | Multisensor Dynamic Waveform Fusion |
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