I mean, audio is just the type of data, it is still represented as an ordered series of points. If i remember on the timeseriesclassification.com website you got quite a lot of audio datasets.
For models, you could look at libraires like sktime, convst, tslearn.
If you don't care about speed or interpretability, I would suggest looking at HIVE COTE 2. If you need faster training, ROCKET or RDST/RDST ensemble (in convst), or simply a 1-NN with DTW, which can represent a baseline.
Did you look only for audio classification papers ? I suspect the time series classification papers could also fit your needs. I can share what I know from recent works if needed.
BruceSwain12 t1_it03jeq wrote
Reply to comment by the_javi_himself in [R] State of the art audio classification by the_javi_himself
I mean, audio is just the type of data, it is still represented as an ordered series of points. If i remember on the timeseriesclassification.com website you got quite a lot of audio datasets.
For models, you could look at libraires like sktime, convst, tslearn.
If you don't care about speed or interpretability, I would suggest looking at HIVE COTE 2. If you need faster training, ROCKET or RDST/RDST ensemble (in convst), or simply a 1-NN with DTW, which can represent a baseline.