Acoustic Scene Classification

37 papers with code • 5 benchmarks • 10 datasets

The goal of acoustic scene classification is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded.

Source: DCASE 2019 Source: DCASE 2018

Most implemented papers

The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification

kkoutini/cpjku_dcase19 3 Jul 2019

To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models.

A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification

edufonseca/icassp19 19 Jun 2018

In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machine.

A multi-device dataset for urban acoustic scene classification

OptimusPrimus/dcase2019_task1b 25 Jul 2018

This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task.

Training neural audio classifiers with few data

jordipons/neural-classifiers-with-few-audio 24 Oct 2018

We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections.

Receptive-field-regularized CNN variants for acoustic scene classification

kkoutini/cpjku_dcase19 5 Sep 2019

One side effect of restricting the RF of CNNs is that more frequency information is lost.

SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

giusenso/seld-tcn 3 Mar 2020

The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars.

Efficient Training of Audio Transformers with Patchout

kkoutini/passt 11 Oct 2021

However, one of the main shortcomings of transformer models, compared to the well-established CNNs, is the computational complexity.

Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

qwenlm/qwen-audio 14 Nov 2023

Recently, instruction-following audio-language models have received broad attention for audio interaction with humans.

Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling

numpde/phonepad 11 Jul 2016

We trained a deep all-convolutional neural network with masked global pooling to perform single-label classification for acoustic scene classification and multi-label classification for domestic audio tagging in the DCASE-2016 contest.

Unsupervised adversarial domain adaptation for acoustic scene classification

shayangharib/AUDASC 17 Aug 2018

A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy.