SOTAVerified

Audio Classification

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 361 papers

TitleStatusHype
MUPAX: Multidimensional Problem Agnostic eXplainable AI—0
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine—0
Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons—0
Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression ClassifierCode0
Adaptive Differential Denoising for Respiratory Sounds ClassificationCode1
Spectrotemporal Modulation: Efficient and Interpretable Feature Representation for Classifying Speech, Music, and Environmental SoundsCode0
Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment LossesCode0
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained NetworksCode0
4,500 Seconds: Small Data Training Approaches for Deep UAV Audio ClassificationCode0
Large Language Models Implicitly Learn to See and Hear Just By Reading—0
Show:102550
← PrevPage 1 of 37Next →
All datasetsAudioSetESC-50ICBHI Respiratory Sound DatabaseVGGSoundSHDFSD50KBalanced Audio SetSpeech CommandsSSCBirdCLEF 2021DCASEEPIC-KITCHENS-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Event-SSMPercentage correct95.9—Unverified
2SNN with Dilated Convolution with Learnable SpacingsPercentage correct95.1—Unverified
3SNN featuring learnable axonal delays with adaptively delay capsPercentage correct92.45—Unverified
4CNNPercentage correct92.4—Unverified
5SNN with spatio-temporal filters and attentionPercentage correct92.4—Unverified
6SNN with temporal-wise attentionPercentage correct91.1—Unverified
7SNNPercentage correct87—Unverified
8Recurrent convolutional SNNPercentage correct83.5—Unverified
9Recurrent SNNPercentage correct83.2—Unverified
10Sparse Spiking Gradient DescentPercentage correct77.5—Unverified