  In this study, we aim to determine if generalized sounds and music can share
a common emotional space, improving predictions of emotion in terms of arousal
and valence. We propose the use of multiple datasets as a multi-domain learning
technique. Our approach involves creating a common space encompassing features
that characterize both generalized sounds and music, as they can evoke emotions
in a similar manner. To achieve this, we utilized two publicly available
datasets, namely IADS-E and PMEmo, following a standardized experimental
protocol. We employed a wide variety of features that capture diverse aspects
of the audio structure including key parameters of spectrum, energy, and
voicing. Subsequently, we performed joint learning on the common feature space,
leveraging heterogeneous model architectures. Interestingly, this synergistic
scheme outperforms the state-of-the-art in both sound and music emotion
prediction. The code enabling full replication of the presented experimental
pipeline is available at https://github.com/LIMUNIMI/MusicSoundEmotions.
