Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation
This paper explores the important role of blind source separation (BSS) techniques in separating <i>M</i> mixtures including <i>N</i> sources using a dual-sensor array, i.e., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/7/1227 |
| _version_ | 1797212719707848704 |
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| author | Jiajia Chen Haijian Zhang Siyu Sun |
| author_facet | Jiajia Chen Haijian Zhang Siyu Sun |
| author_sort | Jiajia Chen |
| collection | DOAJ |
| description | This paper explores the important role of blind source separation (BSS) techniques in separating <i>M</i> mixtures including <i>N</i> sources using a dual-sensor array, i.e., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mo>=</mo><mn>2</mn></mrow></semantics></math></inline-formula>, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than <i>M</i>. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>></mo><mn>2</mn></mrow></semantics></math></inline-formula> sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array. |
| first_indexed | 2024-04-24T10:46:51Z |
| format | Article |
| id | doaj.art-9a07fee4d5a3495ca620c3b18d303df1 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-24T10:46:51Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-9a07fee4d5a3495ca620c3b18d303df12024-04-12T13:17:08ZengMDPI AGElectronics2079-92922024-03-01137122710.3390/electronics13071227Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source SeparationJiajia Chen0Haijian Zhang1Siyu Sun2School of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaThis paper explores the important role of blind source separation (BSS) techniques in separating <i>M</i> mixtures including <i>N</i> sources using a dual-sensor array, i.e., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mo>=</mo><mn>2</mn></mrow></semantics></math></inline-formula>, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than <i>M</i>. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>></mo><mn>2</mn></mrow></semantics></math></inline-formula> sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array.https://www.mdpi.com/2079-9292/13/7/1227underdetermined blind source separationdual-sensormixing matrix estimationsource number estimationtime–frequency sparsity |
| spellingShingle | Jiajia Chen Haijian Zhang Siyu Sun Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation Electronics underdetermined blind source separation dual-sensor mixing matrix estimation source number estimation time–frequency sparsity |
| title | Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation |
| title_full | Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation |
| title_fullStr | Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation |
| title_full_unstemmed | Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation |
| title_short | Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation |
| title_sort | exploiting time frequency sparsity for dual sensor blind source separation |
| topic | underdetermined blind source separation dual-sensor mixing matrix estimation source number estimation time–frequency sparsity |
| url | https://www.mdpi.com/2079-9292/13/7/1227 |
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