# Ensemble Of Deep Neural Networks For Acoustic Scene Classification

Source: [Ensemble Of Deep Neural Networks For Acoustic Scene Classification](<https://www.sota2.com/research/paper/68fa63b91e209131cea00662>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:48:18.501Z

Authors: Venkatesh Duppada, Sushant Hiray

Published: 2017-08-19T09:18:35.000Z

## Abstract

Deep neural networks (DNNs) have recently achieved great success in a multitude of classification tasks. Ensembles of DNNs have been shown to improve the performance. In this paper, we explore the recent state-of-the-art DNNs used for image classification. We modified these DNNs and applied them to the task of acoustic scene classification. We conducted a number of experiments on the TUT Acoustic Scenes 2017 dataset to empirically compare these methods. Finally, we show that the best model improves the baseline score for DCASE-2017 Task 1 by 3.1% in the test set and by 10% in the development set.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1708.05826>)

[PDF](<https://arxiv.org/pdf/1708.05826.pdf>)