scholarly journals Blind Speech Separation in Convolutive Mixtures Using Negentropy Maximization

2010 ◽  
Author(s):  
Vuong Hoang Nam ◽  
Nguyen Quoc Trung ◽  
Tran Hoai Linh
Keyword(s):  
Time Domain ◽  
Main Idea ◽  
New Method ◽  
Speech Separation ◽  
Simulation Experiments ◽  
Convolutive Mixtures ◽  
The Time Domain ◽  
Innovation Processes

This paper proposes a new method to address the  problem  of  blind  speech  separation  in  convolutive mixtures in the time domain. The main idea is extract the  innovation  processes  of  speech  sources  by  nonGaussianity  maximization  and  then  artificially  color them  by  re-coloration  filters.  Some  simulation experiments of the 2x2 case are presented to illustrate the proposed approach.

scholarly journals Active Power Measurement Based on Multiwavelet Transforms

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xiao-Bing Zhang ◽  
Yun-Hui Li ◽  
Xiao-Meng Cui
Keyword(s):  
Time Domain ◽  
Fourier Transforms ◽  
Active Power ◽  
New Method ◽  
Power Measurement ◽  
The Time Domain ◽  
Better Than

This paper discusses a new method for calculating active power in the multiwavelet domain. When the voltage and current waveforms are analyzed using multiwavelet, the active power can be calculated by simply adding the products of the multiwavelet coefficients without having to reconstruct the signals back to the time domain first and then using the traditional integration. From the simulation result, we can see that the results using multiwavelet are better than the ones using wavelet and Fourier Transforms no matter which prefilter is used.

scholarly journals Converting CCGs into typed feature structure grammars

2011 ◽  
Author(s):  
Hans-Ulrich Krieger ◽  
Bernd Kiefer
Keyword(s):  
Time Domain ◽  
Main Idea ◽  
Categorial Grammar ◽  
Space Domain ◽  
Combinatory Categorial Grammar ◽  
Feature Structure ◽  
Pet System ◽  
The Time Domain

In this paper, we report on a transformation scheme that turns a Categorial Grammar, more specifically, a Combinatory Categorial Grammar (CCG; see Baldridge, 2002) into a derivation- and meaning-preserving typed feature structure (TFS) grammar. We describe the main idea which can be traced back at least to work by Karttunen (1986), Uszkoreit (1986), Bouma (1988), and Calder et al. (1988). We then show how a typed representation of complex categories can be extended by other constraints, such as modes, and indicate how the Lambda semantics of combinators is mapped into a TFS representation, using unification to perform perform alpha-conversion and beta-reduction (Barendregt, 1984). We also present first findings concerning runtime measurements, showing that the PET system, originally developed for the HPSG grammar framework, outperforms the OpenCCG parser by a factor of 8–10 in the time domain and a factor of 4–5 in the space domain.

A New Method of Time Series Analysis and its Application to Identifying Modal Parameters

1991 ◽  
Author(s):  
Yusheng He ◽  
Zhaoxiang Deng
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
New Method ◽  
Modal Parameters ◽  
Frequency Domain Method ◽  
Identification Algorithm ◽  
Series Analysis ◽  
The Time Domain ◽  
Modeling Strategy

Abstract In the paper, the attention concentrates on the time domain modal analysis. A new method of time series analysis, which is formed mainly by an ideal modeling strategy and a new COR-IV method, is developed. In addition, an interesting parameter called as modal energy ratio, which is available for design reference, is defined and its identification algorithm is given. The new method presented in this paper and Frequency Domain Method (FDM) are performed on a frame of SG120 vehicle. It is shown by comparison between these two methods that the new method of time series analysis is practical.

Induction Motor Fault Diagnosis Using Voltage Spectrum of Auxiliary Winding and Lissajous Curve of its Park Components

2013 ◽  
Vol 805-806 ◽  
pp. 963-979 ◽  
Author(s):  
Lamiaâ El Menzhi ◽  
Abdallah Saad
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Induction Motor ◽  
Discrete Fourier Transform ◽  
Time Domain ◽  
New Method ◽  
Spectrum Simulation ◽  
Simulation Results ◽  
The Time Domain

In this paper, a new method for induction motor fault diagnosis is presented. It is based on the so-called an auxiliary winding voltage and its Park components. The auxiliary winding is a small coil inserted between two of the stator phases. Expressions of the inserted winding voltage and its Park components are presented. After that, discrete Fourier transform analyzer is required for converting the signals from the time domain to the frequency domain. A Lissajous curve formed of the two Park components is associated to the spectrum. Simulation results curried out for non defected and defected motor show the effectiveness of the proposed method.

scholarly journals Blind Speech Separation in the Frequency Domain Using Assignment Problem Approach

2011 ◽  
Author(s):  
Vuong Hoang Nam ◽  
Nguyen Quoc Trung ◽  
Tran Hoai Linh
Keyword(s):  
Computer Simulation ◽  
Frequency Domain ◽  
Assignment Problem ◽  
Optimal Solution ◽  
Main Difficulty ◽  
Speech Separation ◽  
Simulation Experiments ◽  
Convolutive Mixtures ◽  
Permutation Problem

In this paper, we propose a method for blind speech separation of convolutive mixtures in the frequency domain. The main difficulty in a frequency approach is the so-called permutation problem. In the proposed method, we use the Assignment Problem (AP) approach to solve permuation ambiguity in the frequency domain. In our work, we apply three different algorithms including Hugarian, Jonker-Volgenant and Bertsekas’s Auction to solve the AP to find the optimal solution. Computer simulation experiments are presented to illustrate the proposed method.

scholarly journals Multi-Scale Group Transformer for Long Sequence Modeling in Speech Separation

2020 ◽  
Author(s):  
Yucheng Zhao ◽  
Chong Luo ◽  
Zheng-Jun Zha ◽  
Wenjun Zeng
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Sequence Length ◽  
Separation Performance ◽  
Speech Separation ◽  
Convolutional Network ◽  
Multi Scale ◽  
Sequence Modeling ◽  
The Time Domain

In this paper, we introduce Transformer to the time-domain methods for single-channel speech separation. Transformer has the potential to boost speech separation performance because of its strong sequence modeling capability. However, its computational complexity, which grows quadratically with the sequence length, has made it largely inapplicable to speech applications. To tackle this issue, we propose a novel variation of Transformer, named multi-scale group Transformer (MSGT). The key ideas are group self-attention, which significantly reduces the complexity, and multi-scale fusion, which retains Transform's ability to capture long-term dependency. We implement two versions of MSGT with different complexities, and apply them to a well-known time-domain speech separation method called Conv-TasNet. By simply replacing the original temporal convolutional network (TCN) with MSGT, our approach called MSGT-TasNet achieves a large gain over Conv-TasNet on both WSJ0-2mix and WHAM! benchmarks. Without bells and whistles, the performance of MSGT-TasNet is already on par with the SOTA methods.

scholarly journals The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yanzhu Hu ◽  
Song Wang ◽  
Zhaoyang Wang ◽  
Yixin Zhang
Keyword(s):  
Time Domain ◽  
New Method ◽  
Signal Extraction ◽  
Signal Representation ◽  
Time Dimension ◽  
Excellent Performance ◽  
Vibration Signals ◽  
Time Domain Data ◽  
The Time Domain ◽  
Emd Method

This paper mainly focuses on the representable problem of Φ-OTDR distributed vibration signals. The research included a signal extraction part and a signal representation part. Firstly, in order to extract the better Φ-OTDR signal, the time-domain data should be fully preserved. The 2D-TESP method is used to extract data in this paper. There are 29 characters in the traditional TESP method. The characters’ number is reduced from 29 to 13 and the characters’ dimension is expanded from 1 to 2 in the 2D-TESP method. Secondly, in order to represent Φ-OTDR signal better, the characteristics of Φ-OTDR data and damped vibration signals are combined in the paper. The EMD method and the NMF method are combined to form the new method in the paper. Some parameters in the proposed method are optimized and adjusted by GA method. After Φ-OTDR data is represented by the proposed method, there is excellent performance both on the length dimension and on the time dimension. Lastly, some experiments are carried out according to the physical truth in this paper. The experiments are carried out in the semianechoic room. The methods of the paper have better performance. The methods are proved to be effective through these experiments.

Apodisation in the time domain

1992 ◽  
Vol 2 (4) ◽  
pp. 615-620
Author(s):  
G. W. Series
Keyword(s):  
Time Domain ◽  
The Time Domain
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