Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning

Hutiri, Wiebke; Mathur, Akhil; Ding, Aaron Yi; Kawsar, F.

doi:10.1145/3485730.3493448

Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning

Title

Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning

Author

Hutiri, Wiebke (TU Delft Information and Communication Technology)
Mathur, Akhil (Nokia Bell Labs)
Ding, Aaron Yi (TU Delft Information and Communication Technology)
Kawsar, F. (Nokia Bell Labs)

Date

2021

Abstract

When deploying machine learning (ML) models on embedded and IoT devices, performance encompasses more than an accuracy metric: inference latency, energy consumption, and model fairness are necessary to ensure reliable performance under heterogeneous and resource-constrained operating conditions. To this end, prior research has studied model-centric approaches, such as tuning the hyperparameters of the model during training and later applying model compression techniques to tailor the model to the resource needs of an embedded device. In this paper, we take a data-centric view of embedded ML and study the role that pre-processing parameters in the data pipeline can play in balancing the various performance metrics of an embedded ML system. Through an in-depth case study with audio-based keyword spotting (KWS) models, we show that pre-processing parameter tuning is a remarkable tool that model developers can adopt to trade-off between a model's accuracy, fairness, and system efficiency, as well as to make an embedded ML model resilient to unseen deployment conditions.

Subject

audio keyword spotting
embedded machine learning
fairness
pre-processing parameters

To reference this document use:

http://resolver.tudelft.nl/uuid:5d18bdbd-ae5f-47f5-961a-85506008a815

DOI

https://doi.org/10.1145/3485730.3493448

Publisher

Association for Computing Machinery (ACM)

ISBN

9781450390972

Source

SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems