Furthermore would need to have some audio classifier models. The best models here are CNNs and RNNs. At the moment these are not supported in emlearn. But a Multi-Layer-Perceptron (https://github.com/emlearn/emlearn-micropython/issues/1) can also do a decent job. RandomForest can also be used for simple tasks.
Need to at least expose a spectrogram audio feature extraction. There is code for mel spectrogram in emlearn, so that would be the primary candidate https://emlearn.readthedocs.io/en/stable/eml_audio.html
Furthermore would need to have some audio classifier models. The best models here are CNNs and RNNs. At the moment these are not supported in emlearn. But a Multi-Layer-Perceptron (https://github.com/emlearn/emlearn-micropython/issues/1) can also do a decent job. RandomForest can also be used for simple tasks.
Should also have some simple examples. The "simple audio" tutorial from Tensorflow could be a good starting point, https://www.tensorflow.org/tutorials/audio/simple_audio