paper-with-me

Instrument Recognition 벤치마크

Instrument Recognition on OpenMIC-2018

5개 결과 · ⬇ CSV · JSON

mean average precision

0.843 213.6 426.4 639.2 852 2021-10 2026-09 PaSST — 0.843 (2021-10-11) EAsT-KD + PaSST — 852.0 (2023-06-30) EAsT-Final + PaSST — 847.0 (2023-06-30) DyMN-L — 0.855 (2023-10-24) MATPAC (SSL Model, linear eval) — 0.854 (2025-02-17) PaSST — 0.843 (2021-10-11) EAsT-KD + PaSST — 852.0 (2023-06-30)
RankModel mean average precision Extra Training Data PaperCodeYear
1 DyMN-L 0.855 Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models fschmid56/efficientat 2023
2 MATPAC (SSL Model, linear eval) 0.854 Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning aurianworld/matpac 2025
3 EAsT-KD + PaSST .852 Audio Embeddings as Teachers for Music Classification suncerock/EAsT-music-classification 2023
4 EAsT-Final + PaSST .847 Audio Embeddings as Teachers for Music Classification suncerock/EAsT-music-classification 2023
5 PaSST 0.843 Efficient Training of Audio Transformers with Patchout kkoutini/passt · kkoutini/passt_hear21 2021
1–5 / 5 페이지당 10 20 50 100