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
TUT Acoustic Scenes 2017
TAU Urban Acoustic Scenes 2019
DCASE 2013
DCASE 2019 Mobile
LITIS Rouen
CochlScene
TUT Sound Events 2018
Most implemented papers
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events
hackerekcah/distinct-events-asc
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In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events.
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification
dr-costas/undaw
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A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions.
City classification from multiple real-world sound scenes
In this paper, we undertake the task of automatic city classification to ask whether we can recognize a city from a set of sound scenes?
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification
OptimusPrimus/dcase2019_task1b
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Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning.
Acoustic scene analysis with multi-head attention networks
KrishnaDN/acoustic-scene-analysis-with-multihead-self-attention
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Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns.
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs
We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels.
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
On Task 1b development data set, we achieve an accuracy of 96. 7\% with a model size smaller than 500KB.
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application
The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC), allowing CITISEN to be used as a platform for utilizing and evaluating SE models and flexibly extend the models to address various noise environments and users.
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events
Jungjee/DcaseNet
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Single task deep neural networks that perform a target task among diverse cross-related tasks in the acoustic scene and event literature are being developed.
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.
