scholarly journals REVIEW VOCAL EXTRACTION METHOD IN ACOUSTIC RECORDS

2020 ◽  
pp. 388-390
Author(s):  
Vladyslav Tsaryk ◽  
Viktoriia Hnatushenko
Keyword(s):  
Artificial Intelligence ◽  
Signal Separation ◽  
Musical Composition ◽  
Blind Signal Separation ◽  
Frequency Filtering ◽  
Vocal Signal ◽  
Advantages And Disadvantages ◽  
Blind Signal ◽  
The Voice ◽  
Artificial Intelligence Systems

The problem of blind signal separation, namely, the separation of a vocal track from a finished mixed recording, is considered. The purpose of the research is to isolate the characteristics of the vocal signal on the basis of existing methods and software. The existing methods of vocal selection are analyzed: frequency filtering methods, phase subtraction and methods based on artificial intelligence systems. Features of application of each method, their advantages and disadvantages are highlighted. A comparative analysis of the methods considered using Spleeter and iZotope RX7 software is carried out. Artificial intelligence methods are much better at solving the problem, but they are not satisfactory. There are distortions in the timbre of the voice and foreign noises from the remnants of other instruments. Based on this, we conclude that the existing methods of isolating the vocal are not effective due to the lack of consideration of the peculiarities of the timbre of the voice in a particular musical composition.

A Note on Stone's Conjecture of Blind Signal Separation

2005 ◽  
Vol 17 (2) ◽  
pp. 321-330 ◽  
Author(s):  
Shengli Xie ◽  
Zhaoshui He ◽  
Yuli Fu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
The Novel ◽  
Blind Signal ◽  
Simple Simulation ◽  
Novel Approaches

Stone's method is one of the novel approaches to the blind source separation (BSS) problem and is based on Stone's conjecture. However, this conjecture has not been proved. We present a simple simulation to demonstrate that Stone's conjecture is incorrect. We then modify Stone's conjecture and prove this modified conjecture as a theorem, which can be used a basis for BSS algorithms.

A Method for Extracting Signals with Specific Normalized Kurtosis Range

2013 ◽  
Vol 756-759 ◽  
pp. 3845-3848
Author(s):  
Yong Jian Zhao ◽  
Mei Xia Qu ◽  
Hai Ning Jiang
Keyword(s):  
A Priori ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Validity And Reliability ◽  
Original Source ◽  
Blind Source Extraction ◽  
Blind Signal ◽  
Extraction Algorithm ◽  
Fastica Algorithm ◽  
Source Extraction

The famous FastICA algorithm has been widely used for blind signal separation. For every process, it only converges to an original source which has the maximum negentropy of the underlying signals. To ensure the first output is the desired signal, we incorporate a priori knowledge as a constraint into the FastICA algorithm to construct a robust blind source extraction algorithm. One can extract the desired signal if its normalized kurtosis is known to lie in a specific range, whereas other unwanted signals do not belong to this range. Experimental results on biomedical signals illustrate the validity and reliability of the proposed method.

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