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
Latest papers with no code
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori
In this work, we aim to establish a Bayesian adaptive learning framework by focusing on estimating latent variables in deep neural network (DNN) models.
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers
Based on this observation, we conjecture that suppressing recording device characteristics in the input spectrogram is the most effective.
Domain Information Control at Inference Time for Acoustic Scene Classification
In the Acoustic Scene Classification task (ASC), domain shift is mainly caused by different recording devices.
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration
In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration.
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet
This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome.
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes
Lifelong audio feature extraction involves learning new sound classes incrementally, which is essential for adapting to new data distributions over time.
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
In the second phase, the student network, which presents a low complexity model, is trained with the embeddings extracted from the teacher.
Compressing audio CNNs with graph centrality based filter pruning
For large-scale CNNs such as PANNs designed for audio tagging, our method reduces 24\% computations per inference with 41\% fewer parameters at a slight improvement in performance.
Incremental Learning of Acoustic Scenes and Sound Events
At the same time, its performance on the previous ASC task decreases only by 5. 1 percentage points due to the additional learning of the AT task.
Short-Term Memory Convolutions
In this work we propose novel method for minimization of inference time latency and memory consumption, called Short-Term Memory Convolution (STMC) and its transposed counterpart.
