Improving the representation inverter by exploring more advanced models and/or reaching out to some of the authors

[bs]

It seems very strange to me that the methods work super well for conventional STFT spectrograms but not so well for other reps. One reason why representations might be important for bioacoustic applications is the high sampling rates required. For instance, many dolphin recordings are samples at 96kHz. While it might be possible to downsample a bit and still avoid aliasing, this would lead to an audio input with ~300k elements. However we could also try slicing audio inputs into more reasonable frame lengths and then concatenating results during inference. This I suppose is related to the “Variable Time Scales I’m Vocal Behavior” problem to some degree