  Acoustic scene classification is an automatic listening problem that aims to
assign an audio recording to a pre-defined scene based on its audio data. Over
the years (and in past editions of the DCASE) this problem has often been
solved with techniques known as ensembles (use of several machine learning
models to combine their predictions in the inference phase). While these
solutions can show performance in terms of accuracy, they can be very expensive
in terms of computational capacity, making it impossible to deploy them in IoT
devices. Due to the drift in this field of study, this task has two limitations
in terms of model complexity. It should be noted that there is also the added
complexity of mismatching devices (the audios provided are recorded by
different sources of information). This technical report makes a comparative
study of two different network architectures: conventional CNN and Conv-mixer.
Although both networks exceed the baseline required by the competition, the
conventional CNN shows a higher performance, exceeding the baseline by 8
percentage points. Solutions based on Conv-mixer architectures show worse
performance although they are much lighter solutions.
