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.

比较自动环境声音识别算法在计算成本方面的表现，结果表明在一定的计算成本范围内，深度神经网络能够提供最好的声音分类准确性比率，而高斯混合模型可以以相对较小的成本提供合理的准确性，支持向量机则在准确性和计算成本之间做了妥协。

自动环境声音识别：性能与运算成本比较