Acoustic Scene Classification
41 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
Benchmarks
These leaderboards are used to track progress in Acoustic Scene Classification
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Datasets
MTG-Jamendo
DCASE 2016
TAU Urban Acoustic Scenes 2019
TUT Acoustic Scenes 2017
DCASE 2013
CochlScene
DCASE 2019 Mobile
LITIS Rouen
TUT Sound Events 2018
Most implemented papers
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
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
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
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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
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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
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
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
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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
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
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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
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.
