Datasets:

bpiyush
/

sound-of-water

@@ -30,29 +30,12 @@ This dataset is associated with the paper "The Sound of Water: Inferring Physica
 **TL;DR**: We present a method to infer physical properties of liquids from *just* the sound of pouring. We show in theory how *pitch* can be used to derive various physical properties such as container height, flow rate, etc. Then, we train a pitch detection network (`wav2vec2`) using simulated and real data. The resulting model can predict the physical properties of pouring liquids with high accuracy. The latent representations learned also encode information about liquid mass and container shape.
-## ℹ️ About the dataset
-The dataset is stored in the following directory structure:
-```sh
-SoundOfWater/
-|-- annotations
-|-- assets
-|-- audios
-|-- README.md
-|-- splits
-|-- videos
-`-- youtube_samples
-6 directories, 1 file
-```
 ##  📑 Table of Contents
 - [🚰 The Sound of Water: Inferring Physical Properties from Pouring Liquids](#-the-sound-of-water-inferring-physical-properties-from-pouring-liquids)
-  - [ℹ️ About the dataset](#ℹ️-about-the-dataset)
   - [📑 Table of Contents](#-table-of-contents)
   - [📚 Dataset Overview](#-dataset-overview)
-  - [🎥 Video and audio 🎧 samples](#-video-and-audio--samples)
   - [🗂️ Splits](#️-splits)
   - [📝 Annotations](#-annotations)
       - [Container measurements and other metadata](#container-measurements-and-other-metadata)
@@ -66,9 +49,25 @@ SoundOfWater/
 We collect a dataset of 805 clean videos that show the action of pouring water in a container. Our dataset spans over 50 unique containers made of 5 different materials, 4 different shapes and with hot and cold water. Some example containers are shown below.
-<img width="650" alt="image" src="./assets/containers-v2.png">
-## 🎥 Video and audio 🎧 samples
 The video and audio samples are stored in the `./videos/` and `./audios/` directories, respectively.
 Note that we have trimmed the videos between the precise start and end of the pouring action.

 **TL;DR**: We present a method to infer physical properties of liquids from *just* the sound of pouring. We show in theory how *pitch* can be used to derive various physical properties such as container height, flow rate, etc. Then, we train a pitch detection network (`wav2vec2`) using simulated and real data. The resulting model can predict the physical properties of pouring liquids with high accuracy. The latent representations learned also encode information about liquid mass and container shape.
 ##  📑 Table of Contents
 - [🚰 The Sound of Water: Inferring Physical Properties from Pouring Liquids](#-the-sound-of-water-inferring-physical-properties-from-pouring-liquids)
   - [📑 Table of Contents](#-table-of-contents)
   - [📚 Dataset Overview](#-dataset-overview)
+  - [🎥 Video and 🎧 audio samples](#-video-and--audio-samples)
   - [🗂️ Splits](#️-splits)
   - [📝 Annotations](#-annotations)
       - [Container measurements and other metadata](#container-measurements-and-other-metadata)
 We collect a dataset of 805 clean videos that show the action of pouring water in a container. Our dataset spans over 50 unique containers made of 5 different materials, 4 different shapes and with hot and cold water. Some example containers are shown below.
+<p align="center">
+  <img width="650" alt="image" src="./assets/containers-v2.png">
+</p>
+The dataset is stored in the following directory structure:
+```sh
+SoundOfWater/
+|-- annotations
+|-- assets
+|-- audios
+|-- README.md
+|-- splits
+|-- videos
+`-- youtube_samples
+6 directories, 1 file
+```
+## 🎥 Video and 🎧 audio samples
 The video and audio samples are stored in the `./videos/` and `./audios/` directories, respectively.
 Note that we have trimmed the videos between the precise start and end of the pouring action.