Single channel blind separation of oversampling communication signals based on ICA

2014 ◽  
Author(s):  
Guangkuo Lu ◽  
Manlin Xiao ◽  
Ping Wei ◽  
Jiantao Li
Keyword(s):  
Single Channel ◽  
Blind Separation ◽  
Communication Signals

scholarly journals Sparse pursuit and dictionary learning for blind source separation in polyphonic music recordings

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sören Schulze ◽  
Emily J. King
Keyword(s):  
Single Channel ◽  
Spectral Characteristics ◽  
Musical Instruments ◽  
Model Parameters ◽  
Blind Separation ◽  
Audio Signals ◽  
Frequency Spectra ◽  
Acoustic Instruments ◽  
Invariant Properties ◽  
Music Recordings

AbstractWe propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of pitch. We develop a novel sparse pursuit algorithm that can match the discrete frequency spectra from the recorded signal with the continuous spectra delivered by the model. We first use this algorithm to convert an STFT spectrogram from the recording into a novel form of log-frequency spectrogram whose resolution exceeds that of the mel spectrogram. We then make use of the pitch-invariant properties of that representation in order to identify the sounds of the instruments via the same sparse pursuit method. As the model parameters which characterize the musical instruments are not known beforehand, we train a dictionary that contains them, using a modified version of Adam. Applying the algorithm on various audio samples, we find that it is capable of producing high-quality separation results when the model assumptions are satisfied and the instruments are clearly distinguishable, but combinations of instruments with similar spectral characteristics pose a conceptual difficulty. While a key feature of the model is that it explicitly models inharmonicity, its presence can also still impede performance of the sparse pursuit algorithm. In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset. Additionally, we demonstrate that the dictionary that is constructed for one recording can be applied to a different recording with similar instruments without additional training.

Convolutive Blind Source Separation Applied to the Communication Signals

2012 ◽  
Vol 263-266 ◽  
pp. 188-191
Author(s):  
Xiu Ying Sun ◽  
Peng Fei Xu
Keyword(s):  
Independent Component Analysis ◽  
Time Domain ◽  
Wavelet Decomposition ◽  
Threshold Model ◽  
Automatic Method ◽  
Blind Separation ◽  
Communication Signals ◽  
Communication Signal ◽  
Soft Threshold ◽  
Convolutive Blind Source Separation

In this paper, a method for convolutive blind separation for communication sources is introduced. The method works in time-domain, and it is based on the recently very successful algorithm EFICA for Independent Component Analysis, which is an enhanced version of more famous FastICA. In addition, an automatic method of wavelet de-noising processing is proposed, using the 'mini-maxi' soft-threshold model, wavelet decomposition is performed at level 5 for the noisy separated communication signal, it can improve the performance of BSS system, and this is confirmed in the experiment for communication signals with same carrier frequencies and modulation.

scholarly journals Blind separation of PCMA signals based on neural network

2018 ◽  
Vol 173 ◽  
pp. 03045 ◽  
Author(s):  
WEI Chi ◽  
PENG Hua ◽  
QIU Zeyu
Keyword(s):  
Neural Network ◽  
Frequency Spectrum ◽  
Multiple Access ◽  
Single Channel ◽  
Blind Separation ◽  
Output Data ◽  
Time Frequency ◽  
Characteristic Data ◽  
Lower Complexity ◽  
Separation Model

For paired carrier multiple access (PCMA) signals, a new single-channel blind separation on neural network was proposed. Firstly, the sample waveforms (three symbols) which contains different bit information are constructed, secondly, the time-frequency spectrum of each sample under the different influences of the trailing symbols is Intercepted, finally, the characteristic data of the spectrum as the input data, and the two-bit sequence in each sample as the output data to be trained, network trains these data repeatedly to complete the construction of separation model. The receiver carries on window truncation to the time-frequency spectrum of PCMA signal, neural network recognize the characteristic data of these spectrums to realizes the separation of bit sequences. Experimental results show that this algorithm has lower complexity than PSP algorithm, and the accuracy of it is close to PSP algorithm (L=5).

scholarly journals Blind separation of asymmetric signals based on Synchrosqueezing Wavelet Transform

2018 ◽  
Vol 232 ◽  
pp. 01050
Author(s):  
Chuang Peng ◽  
Xiaojing Yang ◽  
Yu Zhang
Keyword(s):  
Wavelet Transform ◽  
Single Channel ◽  
Satellite Communications ◽  
Signal Interference ◽  
Blind Separation ◽  
Mixed Signal ◽  
Time Frequency ◽  
Strong Signal ◽  
Signal Demodulation ◽  
Better Than

A blind separation algorithm based on synchronous squeezing wavelet transform is proposed to solve the blind separation problem of single channel asymmetric signals in satellite communications. First, the algorithm is used to synchronize the strong signal. Then, the signal time-frequency curve is extracted by synchronous extrusion wavelet transform. Finally, the weak signal interference is filtered out from the mixed signal except the noise and the main frequency of the strong signal. Therefore, the ber of the strong signal demodulation is reduced. The algorithm has the characteristics of blind separation of single channel asymmetric signals without prior information sequence sent by cooperative communicators. The simulation results show that the performance of strong signal demodulation error is better than that of mixed signal direct hard decision.

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