Voice activity detection system for smart earphones
Bibliographic record
Abstract
This paper presents a real-time voice activity detection (VAD) algorithm implemented in a miniature Digital Signal Processor (DSP) for in-ear listening devices such as earphones or headphones. This system allows consumers to hear external speech signals such as public announcements or oral communication while listening to music without removing their listening devices. The proposed algorithm uses two normalized energy features that compare the energy in the frequency region containing speech information with the frequency regions typically containing noise. The extraction of the normalized features represents the key of the proposed VAD since it eliminates the need for a signal-to-noise ratio (SNR) estimator. The VAD's decision is made using two threshold comparison rules computed from the normalized features and a hangover scheme triggered after a given number of observations. The algorithm parameters, namely the frequency regions' boundaries, number of observations, two decision thresholds and hangover's duration, have been optimized offline using a genetic algorithm. The performance of the proposed VAD is compared to a benchmark algorithm in four noise environments and three SNRs. Results show that the average false positive rate (FPR) of the proposed algorithm is 4.2% and the average true positive rate (TPR) is 91.4 % compared to the benchmark algorithm which has a FPR average of 29.9 % and a TPR average of 79.0 %. The proposed VAD is implemented in hardware to validate its reliability and complexity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".