Introduction to Speech Separation Based On Fast ICA
Last Updated :
14 Oct, 2020
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.
Similar Reads
Blind source separation using FastICA in Scikit Learn Independent Component Analysis(ICA) is a method used for separating mixed signals into their original statistically independent components. FastICA is a widely used efficient algorithm for solving this problem especially in Blind Source Separation where the goal is to recover unknown source signals
4 min read
ML | Introduction to Kernel PCA PRINCIPAL COMPONENT ANALYSIS: is a tool which is used to reduce the dimension of the data. It allows us to reduce the dimension of the data without much loss of information. PCA reduces the dimension by finding a few orthogonal linear combinations (principal components) of the original variables wit
6 min read
FastICA on 2D Point Clouds in Scikit Learn In the field of machine learning, the Fast Independent Component Analysis (FastICA) method has emerged as a powerful tool for uncovering latent patterns within data, particularly in the analysis of 2D point clouds derived from sensor or image data. This article provides a thorough exploration of Fas
7 min read
Pattern Recognition | Introduction In this digital world, patterns can be found all around us. They can be seen physically in the colors of the clothing or the rhythm of the speech, or mathematically through the algorithms. In computer science, patterns are represented using vector feature values. And these patterns play an important
6 min read
Audio Data Preparation and Augmentation in Tensorflow Audio is nothing but any type of sound in a digital format. It can be speech, music, environmental sounds, etc. Audio data is commonly used in various fields such as speech recognition, music analysis, audio classification, and more. In recent years, audio data has become an increasingly important d
8 min read
Converting Text to Speech in Java Java Speech API: The Java Speech API allows Java applications to incorporate speech technology into their user interfaces. It defines a cross-platform API to support command and control recognizers, dictation systems and speech synthesizers. Java Speech supports speech synthesis which means the proc
3 min read
Introduction to Audio Computer In our day-to-day life, we most probably use two kinds of computers. They are Smartphones and Personal Computers (PCs). But there exist many other kinds of computers such as Super computers, Mini computers, Mainframe computers, Workstation computers, etc. In this article, you will learn more about a
6 min read
Human - Computer interaction through the ages Introduction - The advent of a technological marvel called the “computer†has revolutionized life in the twenty-first century. From IoT to self-driving cars to smart cities, computers have percolated through the fabric of society. Unsurprisingly the methods with which we interact with computers have
4 min read
Speech Recognition Module Python Speech recognition, a field at the intersection of linguistics, computer science, and electrical engineering, aims at designing systems capable of recognizing and translating spoken language into text. Python, known for its simplicity and robust libraries, offers several modules to tackle speech rec
4 min read
Convert Text to Speech in Python using win32com.client There are several APIs available to convert text to speech in python. One of such APIs available in the python library commonly known as win32com library. It provides a bunch of methods to get excited about and one of them is the Dispatch method of the library. Dispatch method when passed with the a
2 min read