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Updated: Jul 30, 2025

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Explicit-memory multiresolution adaptive framework for speech and music separation.

Ashwin Bellur1, Karan Thakkar1, Mounya Elhilali1

  • 1Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.

EURASIP Journal on Audio, Speech, and Music Processing
|May 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a unified computational framework for sound source separation, mimicking the human auditory system's use of memory and feedback. The model effectively separates speech and music, demonstrating domain-agnostic principles for enhanced auditory perception.

Keywords:
Auditory systemExplicit memoryMulti-scale redundant representationsMusic separationSpeech enhancementTemporal coherence

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Area of Science:

  • Auditory Neuroscience
  • Computational Acoustics
  • Signal Processing

Background:

  • The human auditory system separates sound streams using multi-scale representations, memory, and feedback mechanisms.
  • Existing sound source separation methods often treat speech and music domains separately.

Purpose of the Study:

  • To propose a unified computational framework for sound source separation inspired by human auditory principles.
  • To demonstrate domain-agnostic applicability of sound separation techniques for both speech and music.

Main Methods:

  • Developed an end-to-end computational framework using parallel and hierarchical convolutional paths.
  • Implemented explicit memory and self-feedback mechanisms to refine sound stream selection.
  • Utilized temporal coherence for gating target stream embeddings.

Main Results:

  • Achieved stable sound source separation for both speech and music mixtures.
  • Demonstrated the effectiveness of explicit memory in guiding information selection from complex auditory inputs.
  • Showcased the benefits of feedback mechanisms in improving sound selectivity.

Conclusions:

  • A unified computational framework can effectively perform sound source separation across different domains (speech, music).
  • Explicit memory and feedback are crucial for enhancing auditory selectivity in complex sound environments.
  • The proposed model offers a domain-agnostic approach to sound source separation, mimicking biological auditory processing.