A method performed by one or more computers, the method comprising: obtaining an audio waveform comprising a sequence of audio samples at a first frequency; processing the audio waveform using a machine-learned audio frontend model to generate a feature representation of the audio waveform, wherein the feature representation comprises a sequence of features at a second frequency, and wherein the learned audio frontend model is configured to: apply a machine-learned filtering operation having a plurality of filtering parameters to the audio waveform to generate a filtered representation comprising a sequence of filtered features at the first frequency; apply a machine-learned pooling operation having a plurality of pooling parameters to the filtered representation to generate a pooled representation comprising a sequence of pooled features at the second frequency; and apply a machine-learned normalization operation having a plurality of normalization parameters to the pooled representation to generate the feature representation; and processing the feature representation using an audio understanding machine learning model having a plurality of audio understanding parameters to generate a respective output for each of one or more audio understanding tasks; wherein each filtered feature has a respective value for each of a plurality of channels, and wherein applying a machine-learned pooling operation having a plurality of pooling parameters to the filtered representation to generate a pooled representation comprising a sequence of pooled features at the second frequency comprises: for each channel, applying, with stride greater than one, a respective learned lowpass filter for the channel to the values in the filtered features for the channel to generate a set of pooled values for the channel that have the second frequency; and wherein the respective learned lowpass filter for each channel has a Gaussian impulse response. obtaining an audio waveform comprising a sequence of audio samples at a first frequency; processing the audio waveform using a machine-learned audio frontend model to generate a feature representation of the audio waveform, wherein the feature representation comprises a sequence of features at a second frequency, and wherein the learned audio frontend model is configured to: apply a machine-learned filtering operation having a plurality of filtering parameters to the audio waveform to generate a filtered representation comprising a sequence of filtered features at the first frequency; apply a machine-learned pooling operation having a plurality of pooling parameters to the filtered representation to generate a pooled representation comprising a sequence of pooled features at the second frequency; and apply a machine-learned normalization operation having a plurality of normalization parameters to the pooled representation to generate the feature representation; and apply a machine-learned filtering operation having a plurality of filtering parameters to the audio waveform to generate a filtered representation comprising a sequence of filtered features at the first frequency; apply a machine-learned pooling operation having a plurality of pooling parameters to the filtered representation to generate a pooled representation comprising a sequence of pooled features at the second frequency; and apply a machine-learned normalization operation having a plurality of normalization parameters to the pooled representation to generate the feature representation; and processing the feature representation using an audio understanding machine learning model having a plurality of audio understanding parameters to generate a respective output for each of one or more audio understanding tasks; wherein each filtered feature has a respective value for each of a plurality of channels, and wherein applying a machine-learned pooling operation having a plurality of pooling parameters to the filtered representation to generate a pooled representation comprising a sequence of pooled features at the second frequency comprises: for each channel, applying, with stride greater than one, a respective learned lowpass filter for the channel to the values in the filtered features for the channel to generate a set of pooled values for the channel that have the second frequency; and wherein the respective learned lowpass filter for each channel has a Gaussian impulse response. for each channel, applying, with stride greater than one, a respective learned lowpass filter for the channel to the values in the filtered features for the channel to generate a set of pooled values for the channel that have the second frequency; and wherein the respective learned lowpass filter for each channel has a Gaussian impulse response.