Multi-stage representation learning for blind Room-Acoustic parameter estimation with uncertainty quantification.

Area of Science:

• Acoustic Signal Processing
• Machine Learning
• Environmental Sound Analysis

Background:

• Reverberant recordings pose challenges for accurately identifying acoustic environments.
• Distinguishing source signals from reverberation is a complex problem in audio analysis.

Purpose of the Study:

• To develop a robust method for inferring general representations of acoustic environments from reverberant audio.
• To integrate task-agnostic representation learning with uncertainty quantification for improved audio analysis.

Main Methods:

• A multi-stage approach combining representation learning and uncertainty quantification.
• Utilizing the conformal prediction framework to model estimation errors and inherent ambiguities.
• Employing latent disentanglement analysis for interpretability of learned representations.

Main Results:

• The proposed method demonstrates competitive performance on parameter estimation tasks compared to existing baselines.
• The approach effectively models the ambiguity between source signals and reverberation.
• Learned representations were found to be interpretable, capturing distinct environmental factors.

Conclusions:

• The integrated approach offers a flexible and effective solution for acoustic environment representation from reverberant recordings.
• Uncertainty quantification provides valuable insights into the reliability of audio scene analysis.
• The method's interpretability enhances understanding of learned audio features.