Comparative analysis of speech dereverberation in noisy acoustical environments
Bibliographic record
Abstract
Reverberated speech signals in noisy acoustical environments cause some problems such as reducing speech intelligibility, distinguishing speakers, locating source, quality for hands-free telephony, hearing aid, etc. Adaptive filters can be applied to suppress the interfering signals and reduce the reverberation effects or to dereverberate the received speech signals at microphone. In this paper, Bayesian State-Space Kalman and Wiener filters have been employed to reduce the effect of noise on received speech signal and their results are compared. Also, a dereverberation method is proposed by applying an inverse filter to the received speech signals to reduce the effect of reverberation on received speech signal by single microphone. Different values of the Room Impulse Response are used in the dereverberation procedure and the results are compared. The results show a significant improvement in the quality of the received signal for the desired reverberant environment. Furthermore, the mean opinion score (MOS) of subjective listening quality (LQ) and objective quality score from the perceptual evaluation of speech quality (PESQ) of two different values of RIR coefficient are compared.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".