  This article describes the Data-Efficient Low-Complexity Acoustic Scene
Classification Task in the DCASE 2024 Challenge and the corresponding baseline
system. The task setup is a continuation of previous editions (2022 and 2023),
which focused on recording device mismatches and low-complexity constraints.
This year's edition introduces an additional real-world problem: participants
must develop data-efficient systems for five scenarios, which progressively
limit the available training data. The provided baseline system is based on an
efficient, factorized CNN architecture constructed from inverted residual
blocks and uses Freq-MixStyle to tackle the device mismatch problem. The task
received 37 submissions from 17 teams, with the large majority of systems
outperforming the baseline. The top-ranked system's accuracy ranges from 54.3%
on the smallest to 61.8% on the largest subset, corresponding to relative
improvements of approximately 23% and 9% over the baseline system on the
evaluation set.
