Speech Enhancement Task
tl;dr: Check the full paper here.
On the test-set snippets
Those examples were retrieved from the LibriSpeech (train-clean-100) snippets of 2^16 samples used to test the efficiency of the network. In the next section you can find about the model network applied on multiple SNR intervals and with full temporal-context (complete audio files). Just a quick remainder that the Network was trained on noisy signals with an SNR of 5db-15db.
Noisy Signal | Our Method | Ground Truth |
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On Long temporal context
Here we apply our method to more diverse audios on the full length. The results of this evaluation is applied on ASR algorithms to check if there are performance gains.
SNR between 10db-20db
Noisy Signal | Our Method | Ground Truth |
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SNR between 5db-15db
Noisy Signal | Our Method | Ground Truth |
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SNR between 0db-10db
Noisy Signal | Our Method | Ground Truth |
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ASR Evaluation
@TODO