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

Showing 110 of 361 papers

TitleStatusHype
MUPAX: Multidimensional Problem Agnostic eXplainable AI0
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine0
Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons0
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 Reading0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OmniVec2Test mAP0.56Unverified
2OmniVecTest mAP0.55Unverified
3EquiAVTest mAP0.55Unverified
4MAViL (Audio-Visual, single)Test mAP0.53Unverified
5Audiovisual Masked Autoencoder (Audiovisual, Single)Test mAP0.52Unverified
6CAV-MAE (Audio-Visual)Test mAP0.51Unverified
7BEATs (Audio-only, Ensemble)Test mAP0.51Unverified
8UAVM (Audio + Video)Test mAP0.5Unverified
9SSLAM (Audio-Only, Single)Test mAP0.5Unverified
10mn40_as (Ensemble)Test mAP0.5Unverified