Speech Envelope Dynamics for Noise-Robust Auditory Scene Analysis in Robotics
Bibliographic record
Abstract
Humans make extensive use of auditory cues to interact with other humans, especially in challenging real-world acoustic environments. Multiple distinct acoustic events usually mix together in a complex auditory scene. The ability to separate and localize mixed sound in complex auditory scenes remains a demanding skill for binaural robots. In fact, binaural robots are required to disambiguate and interpret the environmental scene with only two sensors. At the same time, robots that interact with humans should be able to gain insights about the speakers in the environment, such as how many speakers are present and where they are located. For this reason, the speech signal is distinctly important among auditory stimuli commonly found in human-centered acoustic environments. In this paper, we propose a Bayesian method of selectively processing acoustic data that exploits the characteristic amplitude envelope dynamics of human speech to infer the location of speakers in the complex auditory scene. The goal was to demonstrate the effectiveness of this speech-specific temporal dynamics approach. Further, we measure how effective this method is in comparison with more traditional methods based on amplitude detection only.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".