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
Benchmarks
These leaderboards are used to track progress in Acoustic Scene Classification
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Datasets
DCASE 2016
MTG-Jamendo
TAU Urban Acoustic Scenes 2019
TUT Acoustic Scenes 2017
DCASE 2013
DCASE 2019 Mobile
LITIS Rouen
CochlScene
TUT Sound Events 2018
Latest papers with no code
SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms
In this paper, we present SpectNet, an integrated front-end layer that extracts spectrogram features within a CNN architecture that can be used for audio pattern recognition tasks.
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context
In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature.
Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification
Sound event detection (SED) and Acoustic scene classification (ASC) are two widely researched audio tasks that constitute an important part of research on acoustic scene analysis.
Low-complexity CNNs for Acoustic Scene Classification
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC).
Low-complexity CNNs for Acoustic Scene Classification
However, CNNs are resource hungry due to their large size and high computational complexity.
$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features
In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design
The goal of the task is to design an audio scene classification system for device-imbalanced datasets under the constraints of model complexity.
Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework
We conclude that this improvement in ASC performance comes from the regularization effect of using AET and not from the network's improved ability to discern between acoustic events.
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification
While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.
DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations
Due to the drift in this field of study, this task has two limitations in terms of model complexity.
