Air conditioning units (ACUs) are widely used in educational areas like classrooms in order to ensure the occupant's well-being and to control CO₂ concentration (by ventilating using fresh outdoor air). However, the noise level of these ACUs can disrupt the learning environment. Consequently, we propose an approach based on machine learning that is able to distinguish between the different acoustic situations in a classroom, and to dynamically adjust the air volume flow accordingly. To this end, the present algorithm was trained with sound recordings in four different scenarios in a classroom. Both Mel spectrogram and Cochleagram are considered and applied for the task of training the convolutional neural networks (CNN) model, thus enhancing published works in the literature, which only considered the Mel spectrogram. Results show how the Cochleagram is pre-eminent to handle the CNN model training over the Mel spectrogram. Accordingly, we use the Cochleagram for the CNN model training, which is subsequently used for detecting the current situation in the room and adapting the ACU operation to this situation. The results show that running ACUs in high mode provides a learning environment with low CO₂ concentration and high noise. Instead, controlling ACU based scenario predictions made by the CNN model provides a good learning environment with adequate CO₂ concentration and acceptable noise level. Our results demonstrate the effectiveness of using sound classification as a trigger for ventilation control, with the CNN model achieving a good accuracy rate in sound recognition. This underscores the potential of integrating advanced machine learning techniques into building management systems to foster environments that adapt to the needs of their inhabitants automatically. The results of the present work are useful for improving the comfort of the occupants through dynamic ACUs adjustments based on acoustical situations.