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Record W2168622085 · doi:10.1109/aspaa.2007.4393059

Coherent ICA: Implications for Auditory Signal Processing

2007· article· en· W2168622085 on OpenAlexaff
Simon Haykin, Kevin H. M. Kan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInfomaxIndependent component analysisInferior colliculusSpeech recognitionComputer scienceAuditory systemCoding (social sciences)Cochlear nucleusSpeech processingArtificial intelligenceBlind signal separationPattern recognition (psychology)PsychologyMathematicsNeuroscienceNucleusTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we describe a novel algorithm, called Coherent Independent Components Analysis, and referred to as Coherent ICA for short. The algorithm, rooted in information-theoretic learning, exploits the combined use of the Infomax and Imax principles. Experimental results, based on the auditory coding of natural sounds, are presented that demonstrate the ability of coherent ICA to extract the envelope of amplitude-modulated sounds in a manner similar to the behaviour of neurons in the cochlear nucleus and inferior colliculus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.329
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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