A Framework to Measure Reliance of Acoustic Latency on Smartphone Status

Abstract

Audio latency, defined as the time duration when an audio signal travels from the microphone to an app or from an app to the speakers, significantly influences the performance of many mobile sensing applications including acoustic based localization and speech recognition. It is well known within the mobile app development community that audio latencies can be significant (up to hundreds of milliseconds) and vary from smartphone to smartphone and from time to time. Therefore, it is essential to study the causes and effects of the audio latency in smartphones. To the best of our knowledge, there exist mobile apps that can measure audio latency but not the corresponding status of smartphones such as available RAM, CPU loads, battery level, and number of files and folders. In this paper, we are the first to propose a framework that can simultaneously log both the audio latency and the status of smartphones. The proposed framework does not require time synchronization or firmware reprogramming and can run on a standalone device. Since the framework is designed to study the latency causality, the status of smartphones are deliberately and randomly varied as maximum as possible. To evaluate the framework, we present a case study with Android devices. We design and implement a latency app that simultaneously measures the latency and the status of smartphones. The preliminary results show that the latency values have large means (50 − 150 ms) and variances (4−40 ms). The effect of latency can be considerably reduced by just simply subtracting the offset. In order to achieve improved latency prediction that can cope with the variances an advanced regression model would be preferred.