A Modified Single-Channel Blind Separation Method Using EMD and ICA

2014 ◽  
pp. 78-85 ◽  
Author(s):  
Jiao Wang ◽  
Yulin Liu ◽  
Zhichao Chao ◽  
Wei He
Keyword(s):  
Single Channel ◽  
Separation Method ◽  
Blind Separation

scholarly journals Method for the single-channel blind separation based on particle filtering

2021 ◽  
pp. 22-26
Author(s):  
Vasyl Semenov ◽  
Evgeniya Semenova
Keyword(s):  
State Space ◽  
Particle Filtering ◽  
Single Channel ◽  
State Space Model ◽  
Separation Method ◽  
Blind Separation ◽  
Digital Signals ◽  
Space Model

A method for single-channel blind separation of two digital signals is proposed. The method is based on particle filtering and state space model of the mixture of two signals. The effectiveness of method is verified and the advantage over the iterative Bayesian separation method is shown.

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.

Multi-dimensional blind separation method for STBC systems

2013 ◽  
Vol 24 (6) ◽  
pp. 912-918
Author(s):  
Minggang Luo ◽  
Liping Li ◽  
Guobing Qian ◽  
Huaguo Zhang
Keyword(s):  
Separation Method ◽  
Blind Separation

A novel blind source separation method for single-channel signal

2010 ◽  
Vol 90 (12) ◽  
pp. 3232-3241 ◽  
Author(s):  
Hong-Guang Ma ◽  
Qin-Bo Jiang ◽  
Zhi-Qiang Liu ◽  
Gang Liu ◽  
Zhi-Yuan Ma
Keyword(s):  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Separation Method

A Modified Speech Blind Separation Method Based on Information Maximum

2010 ◽  
Vol 5 (12) ◽  
Author(s):  
Xueying Zhang ◽  
Hairong Jia ◽  
Hong Xu
Keyword(s):  
Separation Method ◽  
Blind Separation
Sign in / Sign up

Export Citation Format

Share Document