# Features and Kernels for Audio Event Recognition

arXiv: https://arxiv.org/abs/1607.05765 · HTML: https://mlsanity.com/paper/1607.05765/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2016.

## Built on by (most cited first, 1 of 1)

- [Asymmetric compressive learning guarantees with applications to quantized sketches](https://mlsanity.com/paper/2104.10061/) (2021)

## Abstract (arXiv)

One of the most important problems in audio event detection research is absence of benchmark results for comparison with any proposed method. Different works consider different sets of events and datasets which makes it difficult to comprehensively analyze any novel method with an existing one. In this paper we propose to establish results for audio event recognition on two recent publicly-available datasets. In particular we use Gaussian Mixture model based feature representation and combine them with linear as well as non-linear kernel Support Vector Machines.