blind source separation problem

USAGE SUMMARY

The phrase "blind source separation problem" is correct and usable in written English.
It can be used in contexts related to signal processing, statistics, or machine learning, where the goal is to separate a set of signals from a mixture without prior knowledge of the source signals. Example: "The blind source separation problem is a fundamental challenge in audio processing, where the objective is to isolate individual sound sources from a mixed audio signal."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

12 human-written examples

The mathematical model was an extension of the well-known blind source separation problem, in which original signals need to be separated from a set of mixed signals [ 82], and the algorithm was based on a method used in face recognition software that allows meaningfully learning distinct parts of objects [ 83].

Current Opinion in Genetics and Development

An image blind reconstruction, as a blind source separation problem, has been solved recently by independent component analysis (ICA).

Optics Communications

Commonly, a blind source separation problem is briefly defined by its forward mixing model.

EURASIP Journal on Advances in Signal Processing

User separation is achieved by solving a blind source separation problem.

EURASIP Journal on Wireless Communications and Networking

In blind source separation problem, identifiability relies on the independence of the sources.

EURASIP Journal on Advances in Signal Processing

Obtaining the factorization (varvec{B} varvec{A}), under this assumption, is equivalent to solving a blind source separation problem.

Journal of the Brazilian Computer Society

Human-verified similar examples from authoritative sources

Similar Expressions

48 human-written examples

It is noted in Syed et al. [3] that blind source separation problems are considered tractable if they involve linear mixtures of the sources and that it is necessary to know something about the structure of the sources or something about the mixing matrix that combines them.

Mathematics-in-Industry Case Studies

Thus the practical speech separation problem becomes a convolutive blind source separation (CBSS) problem.

EURASIP Journal on Advances in Signal Processing

The cocktail party problem is a type of blind source separation (BSS) problem that involves unscrambling latent (not observed) signals from a set of mixtures of these signals, without knowing anything about the mixing.

Cell Reports

Independent component analysis (ICA) is a standard statistical tool for solving the blind source separation (BSS) problem.

EURASIP Journal on Advances in Signal Processing

In the literature, one can find a huge number of ICA algorithms to solve the blind source separation (BSS) problem.

EURASIP Journal on Advances in Signal Processing

Expert writing Tips

Best practice

When discussing signal processing or machine learning techniques, clearly define the scope of the "blind source separation problem" you are addressing, specifying any assumptions about the source signals or mixing process to provide context for your work.

Common error

Avoid making overly restrictive assumptions about the statistical independence of source signals without justification. The performance of blind source separation algorithms heavily relies on the validity of these assumptions; therefore, clearly acknowledge any limitations imposed by these assumptions on real-world data.

Linguistic Context

The phrase "blind source separation problem" functions as a noun phrase that identifies a specific challenge in signal processing and related fields. Ludwig AI indicates that it correctly names a known scientific problem. It is used to describe the task of recovering individual source signals from a mixture without prior knowledge.

Expression frequency: Common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "blind source separation problem" is a grammatically sound and commonly used noun phrase, particularly within scientific and technical domains. As confirmed by Ludwig AI, it accurately labels a known challenge in signal processing: recovering source signals from mixtures without prior knowledge. It serves to define and categorize this specific type of problem, often in academic and research contexts. While mostly confined to scientific literature, alternative phrasing such as "unobserved source separation issue" or "unknown source separation challenge" can offer slight variations in emphasis. To effectively address the "blind source separation problem", it's crucial to clearly define the scope, acknowledge assumptions, and be mindful of the limitations, enhancing the clarity and applicability of any proposed solutions.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

unobserved source separation issue

Replaces "blind" with "unobserved" and "problem" with "issue", focusing on the lack of prior knowledge.

unknown source separation challenge

Replaces "blind" with "unknown" and "problem" with "challenge", emphasizing the difficulty.

source separation under uncertainty

Highlights the uncertainty inherent in the separation process when sources are unknown.

blind signal extraction problem

Focuses on extracting signals from a mixture when the sources are not directly observable.

latent source recovery challenge

Uses "latent" to emphasize the hidden nature of the sources and "recovery" to mean retreival of sources.

mixture decomposition problem

Focuses on the decomposition of a mixed signal into its constituent sources.

source unmixing problem

Uses the term "unmixing" as a synonym for separation.

independent component analysis challenge

Highlights a common technique used to solve blind source separation problems.

convolutive blind source separation problem

Specifies the type of mixing process involved.

underdetermined blind source separation

Focuses on the case where there are more sources than sensors.

FAQs

How is the "blind source separation problem" typically addressed?

The "blind source separation problem" is often tackled using techniques like Independent Component Analysis (ICA), Independent Vector Analysis (IVA), and Non-negative Matrix Factorization (NMF). These methods aim to decompose a mixed signal into its constituent sources without prior knowledge of the mixing process.

What are some real-world applications of the "blind source separation problem"?

Applications include audio processing (e.g., separating speech from background noise), biomedical signal processing (e.g., removing artifacts from EEG recordings), and image processing (e.g., separating mixed images). The "cocktail party problem" is a classical example.

What are the limitations of techniques used to solve the "blind source separation problem"?

Limitations often include sensitivity to noise, dependence on the statistical independence of sources, and computational complexity, particularly with large datasets or complex mixing models. Furthermore, permutation and scaling ambiguities are inherent challenges.

What is the difference between "blind source separation" and "source separation"?

"Blind source separation" refers to separating signals from a mixture without knowing the characteristics of the original sources or the mixing process. "Source separation" is a broader term that may include techniques that utilize some prior knowledge about the sources or the mixing environment.