# Listening to Sounds of Silence for Speech Denoising

## Supplementary Material

## Overview

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.

## Synthetic data: AVSPEECH + Audioset

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.

## Synthetic data: AVSPEECH + DEMAND

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.

## Real world data

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).

## Real world data: Audiovisual failed

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.

## Real world data: multi-person

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.

## Other languages

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.

## "The Sound of Silence"

Lastly, listen to "The Sound of Silence" processed by our method that listens to the sounds of silence!