DOI: 10.5061/dryad.r7sqv9smv
title: Data for: Hearable devices with sound bubbles

description: The human auditory system has a limited ability to perceive distance and
 distinguish speakers in crowded settings. A headset technology that can
 create a sound bubble in which all speakers within the bubble are audible,
 but speakers and noise outside the bubble are suppressed, could augment
 human hearing. However, developing such technology is challenging. Here we
 report an intelligent headset system capable of creating sound bubbles.
 The system is based on real-time neural networks that use acoustic data
 from up to six microphones integrated into noise-canceling headsets and
 are run on-device, processing 8 ms audio chunks in 6.36 ms on an embedded
 central processing unit. Our neural networks can generate sound bubbles
 with programmable radii between 1 and 2 meters, and with output signals
 that reduce the intensity of sounds outside the bubble by 49 decibels.
 With previously unseen environments and wearers, our system can focus on
 up to two speakers within the bubble with one to two interfering speakers
 and noiseoutside the bubble. It introduces a headset technology that can
 create a sound bubble in which all speakers within the bubble are audible,
 but speakers and noise outside the bubble are suppressed, which could
 augment human hearing. This repo mainly contains the synthetics dataset
 for results reported in the paper for 1m, 1.5m, and 2m bubble sizes.
description: # Data for: Hearable devices with sound bubbles
 [https://doi.org/10.5061/dryad.r7sqv9smv](https://doi.org/10.5061/dryad.r7sqv9smv) This is the dataset for the paper "Hearable devices with sound bubbles" ## Description of the data and file structure The dataset is used to create a **sound bubble** on a hearable, which can create a sound bubble in which all speakers within the bubble are audible, but speakers and noise outside the bubble are suppressed. The dataset in this repo contained the speech data simulated from pyroomacoustic (https://github.com/LCAV/pyroomacoustics). For each data sample in the train/val/test set contains the following files: metadata.json - JSON file contains all information including room information, microphone array positions, sound source information, and so on. mixture.wav - the wave file for the multi-channel mixture of multiple sources. micsxx_yy.wav - the recording of a single sound source yy at the microphone xx for training target. ## Simulation data for Sound bubble syn_1m.tar - train and val set for 1m bubble evaluation syn_1_5m.tar - train and val set for 1.5m bubble evaluation syn_2m.tar - train and val set for 2m bubble evaluation syn_test.tar - test set for 1m, 1.5m, and 2m bubble evaluations to reproduce the results in Figure 3 ### Files and variables #### File: syn\_1m.tar **Description: train and val set for 1m bubble evaluation**  #### File: syn\_test.tar **Description: test set for 1m, 1.5m, and 2m bubble evaluation to reproduce the results in Figure 3 of the paper**  #### File: syn\_1\_5m.tar **Description: train and val set for 1.5m bubble evaluation**  #### File: syn\_2m.tar **Description: train and val set for 2m bubble evaluation**  ## Code/software Source code including training and evaluation is available in [https://github.com/chentuochao/Sound_Bubble](https://github.com/chentuochao/Sound_Bubble)