Name
Abstract | ||
|---|---|---|
In this letter, we propose a frequency and detector pruning approach for reducing the computational complexity associated with loudness estimation. The frequency pruning approach exploits the principles of psychoacoustics such that the total neural activity is preserved. The detector pruning approach evaluates the excitation/loudness patterns at nonuniform sample locations and employs signal interpolation techniques to obtain their corresponding high resolution estimates. Comparative results with the Moore and Glasberg loudness estimation process reveal that the proposed pruning approach for loudness estimation performs consistently well for different types of audio signals with a significant reduction in the computational complexity. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/LSP.2009.2028413 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
acoustic signal processing,audio signal processing,computational complexity,filtering theory,interpolation,loudness,signal detection,signal resolution,signal sampling,speech processing,Glasberg loudness estimation process,Moore estimation process,audio signal reduction,auditory filter,computational complexity,excitation/loudness pattern,frequency/detector pruning approach,high resolution estimation,nonuniform sample location,psychoacoustics principle,signal interpolation technique,speech processing,Audio coding,loudness,psychoacoustics,speech processing | Loudness,Audio signal,Psychoacoustics,Pattern recognition,Detection theory,Computer science,Interpolation,Artificial intelligence,Audio signal processing,Detector,Computational complexity theory | Journal |
Volume | Issue | ISSN |
16 | 11 | 1070-9908 |
Citations | PageRank | References |
3 | 0.47 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harish Krishnamoorthi | 1 | 13 | 2.46 |
| Andreas S. Spanias | 2 | 528 | 87.90 |
| Visar Berisha | 3 | 76 | 22.38 |