Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Galvas radītā pārvades funkcija× | Neatkarīgā vektoru analīze× | |
|---|---|---|
| Nozare | Lietišķā fizika | Lietišķā fizika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1989 | 2007 |
| Autors≠ | Fredrik Wightman, Doris Kistler | Tae-Won Lee, Mark Lewicki, Terrence Sejnowski |
| Tips≠ | Frequency-dependent spatial filtering function | Multivariate matrix decomposition algorithm |
| Pirmavots≠ | Wightman, F. L., & Kistler, D. J. (1989). Headphone simulation of free-field listening. I: Stimulus synthesis. The Journal of the Acoustical Society of America, 85(2), 858-867. DOI ↗ | Lee, T. W., Lewicki, M. S., & Sejnowski, T. J. (2007). Independent Component Analysis for Source Localization in Biomedical Signals. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 97-100. link ↗ |
| Citi nosaukumi | HRTF, spatial hearing, binaural filter | IVA, multivariate ICA, vector blind source separation |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | The Head-Related Transfer Function (HRTF) describes how the human head, ears, and torso filter sound from different directions. HRTFs capture the acoustical changes that occur as sound travels around the head to reach each ear, enabling the perception of sound location in 3D space. Measured or modeled HRTFs are essential for creating convincing 3D audio through headphones in virtual reality, spatial games, and immersive audio applications. | Independent Vector Analysis (IVA) is a multivariate extension of Independent Component Analysis that jointly separates multiple datasets while maintaining dependencies within each dataset. Developed by Lee, Lewicki, and Sejnowski in the 2000s, IVA is used for blind source separation in multi-channel audio, brain imaging, and signal processing. It exploits both the independence between sources and correlations within frequency bands or time-frequency structures. |
| ScholarGateDatu kopa ↗ |
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