Audio Classification
131 papers with code • 23 benchmarks • 34 datasets
Audio Classification is a machine learning task that involves identifying and tagging audio signals into different classes or categories. The goal of audio classification is to enable machines to automatically recognize and distinguish between different types of audio, such as music, speech, and environmental sounds.
Benchmarks
These leaderboards are used to track progress in Audio Classification
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Libraries
Use these libraries to find Audio Classification models and implementationsDatasets
AudioSet
Speech Commands
ESC-50
VGG-Sound
EPIC-KITCHENS-100
UrbanSound8K
FSD50K
UCR Time Series Classification Archive
Subtasks
Most implemented papers
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
MrtnMndt/OCDVAE_ContinualLearning
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Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge.
Rethinking CNN Models for Audio Classification
kamalesh0406/Audio-Classification
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Besides, we show that even though we use the pretrained model weights for initialization, there is variance in performance in various output runs of the same model.
AST: Audio Spectrogram Transformer
YuanGongND/ast
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In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels.
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions.
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks.
$Π-$nets: Deep Polynomial Neural Networks
grigorisg9gr/polynomial_nets
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Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning.
Generalised Interpretable Shapelets for Irregular Time Series
patrick-kidger/generalised_shapelets
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The shapelet transform is a form of feature extraction for time series, in which a time series is described by its similarity to each of a collection of `shapelets'.
CRNNs for Urban Sound Tagging with spatiotemporal context
multitel-ai/urban-sound-tagging
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This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge.
Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast
satvik-venkatesh/audio-seg-data-synth
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It is useful as a pre-processing step to index, store, and modify audio recordings, radio broadcasts and TV programmes.
Slow-Fast Auditory Streams For Audio Recognition
ekazakos/auditory-slow-fast
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We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs.
