Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability
For dual‐channel time‐frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single‐source points (TF‐...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Electronics and Telecommunications Research Institute (ETRI)
2019-04-01
|
Series: | ETRI Journal |
Subjects: | |
Online Access: | https://doi.org/10.4218/etrij.2018-0581 |
id |
doaj-b3ec46ee32e747ad8d6fe2ef1f6f2638 |
---|---|
record_format |
Article |
spelling |
doaj-b3ec46ee32e747ad8d6fe2ef1f6f26382020-11-25T03:46:26ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632019-04-0141565866910.4218/etrij.2018-058110.4218/etrij.2018-0581Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probabilityZhipeng LiuLichun LiZiru ZhengFor dual‐channel time‐frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single‐source points (TF‐SSP) is used to improve the TF sparsity of each source. For more distinguishability, as the ratios of the coefficients from different columns of the mixing matrix are close, a local peak‐detection mechanism based on interval probability (LPIP) is proposed. LPIP utilizes uniform subintervals to optimize and classify the TF coefficient ratios of the detected TF‐SSP effectively in the case of a high level of TF overlap among sources and reduces the TF interference points and redundant signal features greatly to enhance the estimation accuracy. The simulation results show that under both noiseless and noisy cases, the proposed method performs better than the selected mainstream traditional methods, has good robustness, and has low algorithm complexity.https://doi.org/10.4218/etrij.2018-0581interval probabilitytime‐frequency overlapped signalunderdetermined mixing matrix estimation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhipeng Liu Lichun Li Ziru Zheng |
spellingShingle |
Zhipeng Liu Lichun Li Ziru Zheng Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability ETRI Journal interval probability time‐frequency overlapped signal underdetermined mixing matrix estimation |
author_facet |
Zhipeng Liu Lichun Li Ziru Zheng |
author_sort |
Zhipeng Liu |
title |
Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability |
title_short |
Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability |
title_full |
Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability |
title_fullStr |
Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability |
title_full_unstemmed |
Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability |
title_sort |
mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability |
publisher |
Electronics and Telecommunications Research Institute (ETRI) |
series |
ETRI Journal |
issn |
1225-6463 |
publishDate |
2019-04-01 |
description |
For dual‐channel time‐frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single‐source points (TF‐SSP) is used to improve the TF sparsity of each source. For more distinguishability, as the ratios of the coefficients from different columns of the mixing matrix are close, a local peak‐detection mechanism based on interval probability (LPIP) is proposed. LPIP utilizes uniform subintervals to optimize and classify the TF coefficient ratios of the detected TF‐SSP effectively in the case of a high level of TF overlap among sources and reduces the TF interference points and redundant signal features greatly to enhance the estimation accuracy. The simulation results show that under both noiseless and noisy cases, the proposed method performs better than the selected mainstream traditional methods, has good robustness, and has low algorithm complexity. |
topic |
interval probability time‐frequency overlapped signal underdetermined mixing matrix estimation |
url |
https://doi.org/10.4218/etrij.2018-0581 |
work_keys_str_mv |
AT zhipengliu mixingmatrixestimationmethodfordualchanneltimefrequencyoverlappedsignalsbasedonintervalprobability AT lichunli mixingmatrixestimationmethodfordualchanneltimefrequencyoverlappedsignalsbasedonintervalprobability AT ziruzheng mixingmatrixestimationmethodfordualchanneltimefrequencyoverlappedsignalsbasedonintervalprobability |
_version_ |
1724506566473809920 |