Impact of Face Partitioning On Face Recognition Performance

Dadi, Harihara Santosh; Mohan, Krishna

doi:10.9790/0661-18040190103

Cited by 1 publication

(1 citation statement)

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“…By only hard segmenting the human face (eg., mouth, eye. ), and then independently identifying each segment of the face and integrating them, the recognition accuracy can be improved [12]. (2) Most speech enhancement methods use an L-norm (ℓ1, ℓ2) loss function, for which the contribution of each frequency band to the total loss is identical.…”

Section: Introductionmentioning

confidence: 99%

Sub-Band Knowledge Distillation Framework for Speech Enhancement

Hao,

Wen,

et al. 2020

Preprint

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In single-channel speech enhancement, methods based on fullband spectral features have been widely studied. However, only a few methods pay attention to non-full-band spectral features. In this paper, we explore a knowledge distillation framework based on sub-band spectral mapping for single-channel speech enhancement. Specifically, we divide the full frequency band into multiple sub-bands and pre-train an elite-level sub-band enhancement model (teacher model) for each sub-band. These teacher models are dedicated to processing their own sub-bands. Next, under the teacher models' guidance, we train a general sub-band enhancement model (student model) that works for all sub-bands. Without increasing the number of model parameters and computational complexity, the student model's performance is further improved. To evaluate our proposed method, we conducted a large number of experiments on an open-source data set. The final experimental results show that the guidance from the elite-level teacher models dramatically improves the student model's performance, which exceeds the full-band model by employing fewer parameters.

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Section: Introductionmentioning

confidence: 99%