Core Concepts
Ambisonics regularization affects neural network performance.
Abstract
1. Abstract:
- Ambisonics is a popular spatial audio format.
- Regularization impacts Ambisonics encoding.
- Investigation on regularization impact on DNN training.
2. Introduction:
- Ambisonics signals used for various purposes.
- Encoding errors due to limited microphones.
- Regularization methods proposed for noise control.
3. Ambisonics Encoding:
- Regularized PWD used for encoding.
- Radial functions and noise amplification.
- Various regularization methods explained.
4. Speaker Localization Using DNN-DPD Algorithm:
- DNN-DPD algorithm for speaker localization.
- Direct Path Dominance test for DOA estimation.
- STFT applied to Ambisonics signals.
5. Experimental Investigation:
- Setup details for simulated and measured data.
- Evaluation methodology explained.
- Results show impact of regularization on algorithm performance.
6. Conclusions:
- Uninformed algorithm performance degradation.
- Importance of informed regularization highlighted.
- Future work on regularization-informed approach.
Stats
"Data in the frequency range [400Hz, 5000Hz] was used for processing."
"A regularization level of λ = 0.25 was selected."
"An informed model, is also examined to show the importance of regularization information."
Quotes
"Regularization controls the trade-off between noise amplification and distortion in the Ambisonics signal."
"The figure clearly presents the trade-off between distortion and noise amplification through the choice of λ."