# Automatic Environmental Sound Recognition: Performance versus Computational Cost

arXiv: https://arxiv.org/abs/1607.04589 · HTML: https://mlsanity.com/paper/1607.04589/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2016.

## Built on by (most cited first, 2 of 2)

- [Leveraging Language Model Capabilities for Sound Event Detection](https://mlsanity.com/paper/2308.11530/) (2023)
- [Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting](https://mlsanity.com/paper/2309.15207/) (2023)

## Abstract (arXiv)

In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition (AESR) algorithms are most often developed with limited consideration for computational cost, this article seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance em as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost.