DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics
This work addresses the challenge of monitoring domestic activities for applications in smart home or assistive technologies, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of classifying domestic activities using multi-channel acoustic recordings, by setting up a challenge task with a dataset derived from the SINS database and providing a baseline neural network system to lower participation hurdles and establish reference performance.
The DCASE 2018 Challenge consists of five tasks related to automatic classification and detection of sound events and scenes. This paper presents the setup of Task 5 which includes the description of the task, dataset and the baseline system. In this task, it is investigated to which extent multi-channel acoustic recordings are beneficial for the purpose of classifying domestic activities. The goal is to exploit spectral and spatial cues independent of sensor location using multi-channel audio. For this purpose we provided a development and evaluation dataset which are derivatives of the SINS database and contain domestic activities recorded by multiple microphone arrays. The baseline system, based on a Neural Network architecture using convolutional and dense layer(s), is intended to lower the hurdle to participate the challenge and to provide a reference performance.