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Introduction to Speech Separation Based On Fast ICA

Last Updated : 14 Oct, 2020
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Prerequisite: ML | Independent Component Analysis

What is Speech Separation?

Speech Separation is the process of extracting all overlapping speech sources from a given mixed speech signal. Speech Separation is a special scenario for source separation problems, where the focus is only on overlapping speech signal sources. Speech Separation is implemented using Independent Component Analysis (ICA). Where FastICA is an effective and common algorithm for independent component analysis where this strategy formulates a problem of speech separation, where speech patterns, speakers, and background noise can be recognized.

Cocktail Party Problem: Natural auditory environments, whether cocktail parties or rain forests contain a lot of things that create sounds at the same time. The Cocktail Party Problem is the task of listening to a sound of interest, often a speech signal, in this kind of complex auditory environment.

What is ICA?

One of the most widely used examples of blind source separation is separating voice signals from people speaking simultaneously, this is called the cocktail party issue. The Independent component analysis (ICA)  is one of the most well-known techniques used to solve this problem. The aim of this problem is to detect or extract the sound with a single object even if different sounds in the environment are superimposed on each other. The independent component analysis focuses on independence, i.e. independent components.

As people, we have the ability to recognize and concentrate on particular communication topics of our choice. How are we supposed to do that? And how can we program this to a computer?

The method called FastICA is an efficient and popular algorithm for independent component analysis, which is used to solve the cocktail party problem. 

What is FastICA?

Solving Cocktail Party Problem FastICA is a way of separating signals that have multivariate data into their additive subcomponents using statistical methods to separate a single voice signal from a mixture of sounds like other voices and background noise. The FastICA algorithm is a highly efficient computational method for performing ICA estimation. It uses a fixed-point iteration scheme that is 10-100 times faster than conventional gradient descent methods for ICA in the independent experiments. Another advantage of the FastICA algorithm is that it can also be used for projection pursuit, thus providing a general-purpose data analysis method that can be used both in an exploratory manner and for the estimation of independent components (or sources).

Properties of the FastICA Algorithm :

Compared to existing ICA methods the FastICA algorithm has several desirable properties.

  • The FastICA has most of the benefits of neural algorithms: it is parallel, distributed, computationally simple, requires very less memory space.
  • Independent components can be estimated one by one, which is roughly equivalent to the pursuance of projection. This is useful in exploratory data analysis and reduces the method’s computational load in circumstances where it is only necessary to estimate some of the independent components.
  • This algorithm finds directly independent components of (practically) any non-Gaussian distribution by using non-linearity.
  • The performance of the method can be optimized by selecting the appropriate non-linearity.


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