GTZAN and CREMA-D

[yunzqq]

Hi, Could you please provide the split setting on these two datasets? Thank you!

[daisukelab]

Hi, thanks for your interest.

You can find them in the evaluation package EVAR. Please follow: https://github.com/nttcslab/eval-audio-repr/blob/main/Preparing-datasets.md

Data splits can be found in evar/metadata/*.csv

Hope it helps.

[yunzqq]

Hi, thanks for your interest.

You can find them in the evaluation package EVAR. Please follow: https://github.com/nttcslab/eval-audio-repr/blob/main/Preparing-datasets.md

Data splits can be found in evar/metadata/*.csv

Hope it helps.

Many thanks!

[daisukelab]

Please let us know if you publish your paper in the future!

[yunzqq]

Please let us know if you publish your paper in the future!

hhh, OK!

[yunzqq]

Please let us know if you publish your paper in the future!

May I ask another question? For the long audio recording, how long clips are made for training? and At inference time, the audio recording is split into clips and averaged the logit is used for final classification results?

Best, Qiquan

[daisukelab]

May I ask another question? For the long audio recording, how long clips are made for training? and At inference time, the audio recording is split into clips and averaged the logit is used for final classification results?

Thank you for your question! A quick answer is you would be correct.

• While pre-training, we randomly crop a fixed duration from training samples.
• At inference time, we do the following:

And the runtime implementation is as follows:

https://github.com/nttcslab/m2d/blob/4cdffb04b3fb311fc64a87021ccc3cacf7a01ceb/m2d/runtime_audio.py#L173-L225

Please let me know if you have any more questions.

[yunzqq]

Many thanks for your help！Are you in Greece for ICASSP 2023？If yes May I communicate with you ？ hahaha

[daisukelab]

Yes, I'm presenting this paper:

AASP-P4: Anomaly Detection and Representation Learning for Audio Classification Room: Poster Area 2 - Garden Type: Poster 03:35 PM to 5:05 PM 1773 (AASP-P4.3): MASKED MODELING DUO: LEARNING REPRESENTATIONS BY ENCOURAGING BOTH NETWORKS TO MODEL THE INPUT

See you there! :)

[yunzqq]

Yes, I'm presenting this paper:

AASP-P4: Anomaly Detection and Representation Learning for Audio Classification Room: Poster Area 2 - Garden Type: Poster 03:35 PM to 5:05 PM 1773 (AASP-P4.3): MASKED MODELING DUO: LEARNING REPRESENTATIONS BY ENCOURAGING BOTH NETWORKS TO MODEL THE INPUT

See you there! :)

Many thanks! See you there! Best,

Qiquan Zhang