Name
Abstract | ||
|---|---|---|
A novel noise power spectral density (PSD) estimator for disturbed speech signals which operates in the short-time Fourier domain is presented. A noise PSD estimate is provided by constrained tracing with time of the noisy observation separately for each frequency bin. The constraint is a limitation of the logarithmic magnitude change between successive time frames. Since speech onset is assumed as sudden rises in the noisy observation, a fixed and adaptive tracing parameter beta has been derived to track the contained noise while preventing speech leakage to the noise PSD estimate. The experimental evaluation and comparison with state-of-the-art algorithms, SPP and Minimum Statistics, confirms a lower logarithmic noise estimation error and superior speech enhancement rated in a standard noise reduction system. The proposed concept has extremely low computational complexity and memory usage. Thus, it is well suited for applications where processing power and memory is limited. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Noise power estimation, speech enhancement, noise reduction, low complexity, low memory |
Field | DocType | ISSN |
Value noise,Colors of noise,Noise floor,Pattern recognition,Noise measurement,Computer science,Salt-and-pepper noise,Noise spectral density,Artificial intelligence,Gaussian noise,Gradient noise | Conference | 1520-6149 |
Citations | PageRank | References |
3 | 0.43 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Heese | 1 | 50 | 5.25 |
| Peter Vary | 2 | 852 | 75.52 |