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 1–10 of 361 papers
All datasetsAudioSetESC-50ICBHI Respiratory Sound DatabaseVGGSoundSHDFSD50KBalanced Audio SetSpeech CommandsSSCBirdCLEF 2021DCASEEPIC-KITCHENS-100
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ADD | ICBHI Score | 65.53 | — | Unverified |
| 2 | BEATs (PAFA) | ICBHI Score | 64.84 | — | Unverified |
| 3 | BTS | ICBHI Score | 63.54 | — | Unverified |
| 4 | BEATs (CE) | ICBHI Score | 63.49 | — | Unverified |
| 5 | M2D-X/0.7 (η=0.3) | ICBHI Score | 63.29 | — | Unverified |
| 6 | CycleGuardian | ICBHI Score | 63.26 | — | Unverified |
| 7 | M2D/0.7 (e=0.3) | ICBHI Score | 62.73 | — | Unverified |
| 8 | Audio-CLAP | ICBHI Score | 62.56 | — | Unverified |
| 9 | AST (Patch-Mix CL) | ICBHI Score | 62.37 | — | Unverified |
| 10 | AFT on Mixed-500 | ICBHI Score | 61.79 | — | Unverified |