In this paper a blind source separation algorithm in reverberant environment is presented. The algorithms working in such adverse environments are usually characterized by a huge computational cost. In order to reduce...
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ISBN:
(纸本)9781607500728
In this paper a blind source separation algorithm in reverberant environment is presented. The algorithms working in such adverse environments are usually characterized by a huge computational cost. In order to reduce the computational complexity of this kind of algorithms a partitioned frequencydomain approach is proposed. Several experimental results are shown to demonstrate the effectiveness of the proposed method.
The least mean squared (LMS) algorithm and its variants have been the most often used algorithms in adaptive signal processing. However the LMS algorithm suffers from a high computational complexity, especially with l...
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The least mean squared (LMS) algorithm and its variants have been the most often used algorithms in adaptive signal processing. However the LMS algorithm suffers from a high computational complexity, especially with large filter lengths. The Fourier transform-based block normalized LMS (FBNLMS) reduces the computation count by using the discrete Fourier transform (DFT) and exploiting the fast algorithms for implementing the DFT. Even though the savings achieved with the FBNLMS over the direct-LMS implementation are significant, the computational requirements of FBNLMS are still very high, rendering many real-time applications, like audio and video estimation, infeasible. The Hartley transform-based BNLMS (HBNLMS) is found to have a computational complexity much less than, and a memory requirement almost of the same order as, that of the FBNLMS. This paper is based on the cosine and sine symmetric implementation of the discrete Hartley transform (DHT), which is the key in-reducing the computational complexity of the FBNLMS by 33% asymptotically. (with respect to multiplications). The parallel implementation of the discrete cosine transform (DCT) in turn can lead to more efficient implementations of the HBNLMS.
In this paper a blind source separation algorithm in convolutive environment is presented. In order to avoid the classical permutation ambiguity in the frequencydomain solution, a geometrical constraint is considered...
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ISBN:
(纸本)9781607506928;9781607506911
In this paper a blind source separation algorithm in convolutive environment is presented. In order to avoid the classical permutation ambiguity in the frequencydomain solution, a geometrical constraint is considered. Moreover a beamformer algorithm is integrated with the proposed solution: in this way the directivity pattern of the proposed architecture can take into account the residual permutation at low frequencies and the scaling inconsistency. Several experimental results are shown to demonstrate the effectiveness of the proposed method.
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