Bibliographic Details
| Title: |
Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables |
| Authors: |
Veluri, Bandhav, Itani, Malek, Chan, Justin, Yoshioka, Takuya, Gollakota, Shyamnath |
| Publication Year: |
2023 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing |
| More Details: |
Imagine being able to listen to the birds chirping in a park without hearing the chatter from other hikers, or being able to block out traffic noise on a busy street while still being able to hear emergency sirens and car honks. We introduce semantic hearing, a novel capability for hearable devices that enables them to, in real-time, focus on, or ignore, specific sounds from real-world environments, while also preserving the spatial cues. To achieve this, we make two technical contributions: 1) we present the first neural network that can achieve binaural target sound extraction in the presence of interfering sounds and background noise, and 2) we design a training methodology that allows our system to generalize to real-world use. Results show that our system can operate with 20 sound classes and that our transformer-based network has a runtime of 6.56 ms on a connected smartphone. In-the-wild evaluation with participants in previously unseen indoor and outdoor scenarios shows that our proof-of-concept system can extract the target sounds and generalize to preserve the spatial cues in its binaural output. Project page with code: https://semantichearing.cs.washington.edu |
| Document Type: |
Working Paper |
| DOI: |
10.1145/3586183.3606779 |
| Access URL: |
http://arxiv.org/abs/2311.00320 |
| Accession Number: |
edsarx.2311.00320 |
| Database: |
arXiv |
