# Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds

arXiv: https://arxiv.org/abs/2109.02763 · HTML: https://mlsanity.com/paper/2109.02763/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 84 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 30 of 42)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Audio-Visual Scene Analysis with Self-Supervised Multisensory Features](https://mlsanity.com/paper/1804.03641/) (2018)
- [Self-Supervised Learning by Cross-Modal Audio-Video Clustering](https://mlsanity.com/paper/1911.12667/) (2019)
- [The Sound of Pixels](https://mlsanity.com/paper/1804.03160/) (2018)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Learning to Localize Sound Source in Visual Scenes](https://mlsanity.com/paper/1803.03849/) (2018)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [The Sound of Motions](https://mlsanity.com/paper/1904.05979/) (2019)
- [Talk2Car: Taking Control of Your Self-Driving Car](https://mlsanity.com/paper/1909.10838/) (2019)
- [Visually Indicated Sounds](https://mlsanity.com/paper/1512.08512/) (2015)
- [Learning to Separate Object Sounds by Watching Unlabeled Video](https://mlsanity.com/paper/1804.01665/) (2018)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (2018)
- [Co-Separating Sounds of Visual Objects](https://mlsanity.com/paper/1904.07750/) (2019)
- [Look, Listen, and Act: Towards Audio-Visual Embodied Navigation](https://mlsanity.com/paper/1912.11684/) (2019)
- [Learning Representations from Audio-Visual Spatial Alignment](https://mlsanity.com/paper/2011.01819/) (2020)
- [Self-Supervised Generation of Spatial Audio for 360 Video](https://mlsanity.com/paper/1809.02587/) (2018)
- [Semantic Audio-Visual Navigation](https://mlsanity.com/paper/2012.11583/) (2020)
- [Robust Audio-Visual Instance Discrimination](https://mlsanity.com/paper/2103.15916/) (2021)
- [Ambient Sound Provides Supervision for Visual Learning](https://mlsanity.com/paper/1608.07017/) (2016)
- [2.5D Visual Sound](https://mlsanity.com/paper/1812.04204/) (2018)
- [Self-supervised Moving Vehicle Tracking with Stereo Sound](https://mlsanity.com/paper/1910.11760/) (2019)
- [VisualEchoes: Spatial Image Representation Learning through Echolocation](https://mlsanity.com/paper/2005.01616/) (2020)
- [Sep-Stereo: Visually Guided Stereophonic Audio Generation by Associating Source Separation](https://mlsanity.com/paper/2007.09902/) (2020)
- [Distilling Audio-Visual Knowledge by Compositional Contrastive Learning](https://mlsanity.com/paper/2104.10955/) (2021)

## Built on by (most cited first, 4 of 4)

- [Sound Localization by Self-Supervised Time Delay Estimation](https://mlsanity.com/paper/2204.12489/) (2022)
- [An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits](https://mlsanity.com/paper/2212.10744/) (2022)
- [Segment Beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation](https://mlsanity.com/paper/2312.08673/) (2023)
- [AVS-Net: Audio-Visual Scale Net for Self-supervised Monocular Metric Depth Estimation](https://mlsanity.com/paper/2412.01637/) (2024)

## Abstract (arXiv)

Humans can robustly recognize and localize objects by using visual and/or auditory cues. While machines are able to do the same with visual data already, less work has been done with sounds. This work develops an approach for scene understanding purely based on binaural sounds. The considered tasks include predicting the semantic masks of sound-making objects, the motion of sound-making objects, and the depth map of the scene. To this aim, we propose a novel sensor setup and record a new audio-visual dataset of street scenes with eight professional binaural microphones and a 360-degree camera. The co-existence of visual and audio cues is leveraged for supervision transfer. In particular, we employ a cross-modal distillation framework that consists of multiple vision teacher methods and a sound student method -- the student method is trained to generate the same results as the teacher methods do. This way, the auditory system can be trained without using human annotations. To further boost the performance, we propose another novel auxiliary task, coined Spatial Sound Super-Resolution, to increase the directional resolution of sounds. We then formulate the four tasks into one end-to-end trainable multi-tasking network aiming to boost the overall performance. Experimental results show that 1) our method achieves good results for all four tasks, 2) the four tasks are mutually beneficial -- training them together achieves the best performance, 3) the number and orientation of microphones are both important, and 4) features learned from the standard spectrogram and features obtained by the classic signal processing pipeline are complementary for auditory perception tasks. The data and code are released.