Development of a speech separation system using frequency domain blind source separation technique

Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech,...
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Autor*in:	Sharma, Bhuvnesh Kumar [verfasserIn] Kumar, Mithilesh Meena, R. S.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Teleconferencing Frequency domain blind source separation technique Frequency domain analysis Speech signal

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 83(2023), 11 vom: 23. Sept., Seite 32857-32872
Übergeordnetes Werk:	volume:83 ; year:2023 ; number:11 ; day:23 ; month:09 ; pages:32857-32872

Links:	Volltext

DOI / URN:	10.1007/s11042-023-16600-6

Katalog-ID:	SPR055068723

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520			\|a Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system.
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allfields	10.1007/s11042-023-16600-6 doi (DE-627)SPR055068723 (SPR)s11042-023-16600-6-e DE-627 ger DE-627 rakwb eng Sharma, Bhuvnesh Kumar verfasserin aut Development of a speech separation system using frequency domain blind source separation technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. Teleconferencing (dpeaa)DE-He213 Frequency domain blind source separation technique (dpeaa)DE-He213 Frequency domain analysis (dpeaa)DE-He213 Speech signal (dpeaa)DE-He213 Kumar, Mithilesh aut Meena, R. S. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 11 vom: 23. Sept., Seite 32857-32872 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:11 day:23 month:09 pages:32857-32872 https://dx.doi.org/10.1007/s11042-023-16600-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 11 23 09 32857-32872
spelling	10.1007/s11042-023-16600-6 doi (DE-627)SPR055068723 (SPR)s11042-023-16600-6-e DE-627 ger DE-627 rakwb eng Sharma, Bhuvnesh Kumar verfasserin aut Development of a speech separation system using frequency domain blind source separation technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. Teleconferencing (dpeaa)DE-He213 Frequency domain blind source separation technique (dpeaa)DE-He213 Frequency domain analysis (dpeaa)DE-He213 Speech signal (dpeaa)DE-He213 Kumar, Mithilesh aut Meena, R. S. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 11 vom: 23. Sept., Seite 32857-32872 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:11 day:23 month:09 pages:32857-32872 https://dx.doi.org/10.1007/s11042-023-16600-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 11 23 09 32857-32872
allfields_unstemmed	10.1007/s11042-023-16600-6 doi (DE-627)SPR055068723 (SPR)s11042-023-16600-6-e DE-627 ger DE-627 rakwb eng Sharma, Bhuvnesh Kumar verfasserin aut Development of a speech separation system using frequency domain blind source separation technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. Teleconferencing (dpeaa)DE-He213 Frequency domain blind source separation technique (dpeaa)DE-He213 Frequency domain analysis (dpeaa)DE-He213 Speech signal (dpeaa)DE-He213 Kumar, Mithilesh aut Meena, R. S. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 11 vom: 23. Sept., Seite 32857-32872 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:11 day:23 month:09 pages:32857-32872 https://dx.doi.org/10.1007/s11042-023-16600-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 11 23 09 32857-32872
allfieldsGer	10.1007/s11042-023-16600-6 doi (DE-627)SPR055068723 (SPR)s11042-023-16600-6-e DE-627 ger DE-627 rakwb eng Sharma, Bhuvnesh Kumar verfasserin aut Development of a speech separation system using frequency domain blind source separation technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. Teleconferencing (dpeaa)DE-He213 Frequency domain blind source separation technique (dpeaa)DE-He213 Frequency domain analysis (dpeaa)DE-He213 Speech signal (dpeaa)DE-He213 Kumar, Mithilesh aut Meena, R. S. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 11 vom: 23. Sept., Seite 32857-32872 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:11 day:23 month:09 pages:32857-32872 https://dx.doi.org/10.1007/s11042-023-16600-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 11 23 09 32857-32872
allfieldsSound	10.1007/s11042-023-16600-6 doi (DE-627)SPR055068723 (SPR)s11042-023-16600-6-e DE-627 ger DE-627 rakwb eng Sharma, Bhuvnesh Kumar verfasserin aut Development of a speech separation system using frequency domain blind source separation technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. Teleconferencing (dpeaa)DE-He213 Frequency domain blind source separation technique (dpeaa)DE-He213 Frequency domain analysis (dpeaa)DE-He213 Speech signal (dpeaa)DE-He213 Kumar, Mithilesh aut Meena, R. S. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 11 vom: 23. Sept., Seite 32857-32872 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:11 day:23 month:09 pages:32857-32872 https://dx.doi.org/10.1007/s11042-023-16600-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 11 23 09 32857-32872
language	English
source	Enthalten in Multimedia tools and applications 83(2023), 11 vom: 23. Sept., Seite 32857-32872 volume:83 year:2023 number:11 day:23 month:09 pages:32857-32872
sourceStr	Enthalten in Multimedia tools and applications 83(2023), 11 vom: 23. Sept., Seite 32857-32872 volume:83 year:2023 number:11 day:23 month:09 pages:32857-32872
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Teleconferencing Frequency domain blind source separation technique Frequency domain analysis Speech signal
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Sharma, Bhuvnesh Kumar @@aut@@ Kumar, Mithilesh @@aut@@ Meena, R. S. @@aut@@
publishDateDaySort_date	2023-09-23T00:00:00Z
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author	Sharma, Bhuvnesh Kumar
spellingShingle	Sharma, Bhuvnesh Kumar misc Teleconferencing misc Frequency domain blind source separation technique misc Frequency domain analysis misc Speech signal Development of a speech separation system using frequency domain blind source separation technique
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topic_title	Development of a speech separation system using frequency domain blind source separation technique Teleconferencing (dpeaa)DE-He213 Frequency domain blind source separation technique (dpeaa)DE-He213 Frequency domain analysis (dpeaa)DE-He213 Speech signal (dpeaa)DE-He213
topic	misc Teleconferencing misc Frequency domain blind source separation technique misc Frequency domain analysis misc Speech signal
topic_unstemmed	misc Teleconferencing misc Frequency domain blind source separation technique misc Frequency domain analysis misc Speech signal
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title	Development of a speech separation system using frequency domain blind source separation technique
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title_full	Development of a speech separation system using frequency domain blind source separation technique
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author_browse	Sharma, Bhuvnesh Kumar Kumar, Mithilesh Meena, R. S.
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title_sort	development of a speech separation system using frequency domain blind source separation technique
title_auth	Development of a speech separation system using frequency domain blind source separation technique
abstract	Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Development of a speech separation system using frequency domain blind source separation technique
url	https://dx.doi.org/10.1007/s11042-023-16600-6
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author2	Kumar, Mithilesh Meena, R. S.
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR055068723</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240309064705.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240309s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-023-16600-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR055068723</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-023-16600-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sharma, Bhuvnesh Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Development of a speech separation system using frequency domain blind source separation technique</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones, tablets, and other virtual devices. Even though researchers are adopting a variety of blind source techniques to separate and recognize speech, the problem and the greatest difficulty still lie in assuming that communication comes from multiple speakers. The process of extracting target speech from background noise is known as speech separation. In this research, a speech separation system using the frequency domain Blind source separation technique (BSS technique) is used for the separation of the original speech signals from the user. Frequency domain analysis is used for the comprehensive analysis of the signal properties and along with that the frequency range of the signals in the room is also determined. Determining the frequential spectra of the sources included in a sound recording as well as their temporal activations helps in improving the speech signal of the user. Blind source techniques help in retrieving the original sound by eliminating external noises and selecting the best frequency signal. The total impulse response of the system is evaluated The speech signals along with the Room Impulse Response, magnitude, and Phasor plot are graphically represented for the efficient analysis of the system.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teleconferencing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequency domain blind source separation technique</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Frequency domain analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Speech signal</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kumar, Mithilesh</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meena, R. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995</subfield><subfield code="g">83(2023), 11 vom: 23. 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