Listening to Sounds of Silence for Speech Denoising
Supplementary Material
This offline webpage organizes our speech denoising results.
We first show the
comparison results on AudioSet dataset, DEMAND dataset and real-world
audio recordings.
When demonstrating with real-world recordings, we also include
scenarios in which audiovisual denoising would fail because of the lack of frontal faces
in video footage as well as scenarios in which multiple speakers present.
Both are common in daily life.
We then present examples of our denoising results in other
languages, which are all resulted from our model trained solely in English.
Lastly, we show two clips from the song "The Sound of
Silence", as an echo of our title, to further demonstrate our model's ability to
reduce non-stationary noise like music.
Please click each item below to see and hear individual audio results.
Here we show denoising results on
synthetic input signals.
The input signals are generated using audio clips in
AVSPEECH as foreground speech and
in AudioSet as background noise.
With seven different input SNRs (from -10dB to 10dB),
we compare the denoising results of our model with other methods.
Please note that here the Ours-GTSI method uses ground-truth silent interval
labels. It is by no means a practical approach. Rather, it is meant to
show the best possible (upper-bound) denoising quality when the silent intervals are perfectly
predicted.
Here we provide comparison results
similar to the previous section.
Instead of using AudioSet data as background noise,
here we use DEMAND, another dataset used in previous denoising works,
as the source of background noise. All other setups are the same as in the previous section.
Here we provide denoising results on
real-world recordings, in comparison to other methods.
These examples are recorded in video with a single person
from the front-facing view.
Only the (mono-channel) audio signals in the recordings
are provided to the denoising methods except for VSE,
which requires audiovisual input (i.e., video footage is also provided as input).
Here we present more denoising results on
real-world recordings. Unlike those presented above,
the examples here all have human faces either blocked (e.g., by facial masks during the COVID-19 pandemic)
or excluded from the camera's field of view.
As a result, the audiovisual method (i.e., VSE) becomes not applicable,
and our experiments show that VSE fails in these cases. We therefore exclude it from
the compared methods.
Here we show denoising results on real-world recordings in which
multiple people speak. Our method is able to
suppress background noise while retaining the voices of all speakers.
Here we show our denoising results on speeches
in other languages.
The silent interval detection results are also presented.
In these examples, our model is trained with speeches only in English, the same as that used in previous examples.
No additional training is conducted.
Lastly, listen to "The Sound of Silence" processed by our method that listens to the sounds of silence!