The performance of most emotion recognition systems degrades in real-life situations ("in the wild" scenarios) where the audio is contaminated by reverberation. Our study explores new methods to alleviate the performance degradation of Speech Emotion Recognition (SER) algorithms and develop a more robust system for adverse conditions. We propose processing multi-microphone signals to address these challenges and improve emotion classification accuracy. We adopt a state-of-the-art transformer model, the Hierarchical Token-semantic Audio Transformer (HTS-AT), to handle multi-channel audio inputs. We evaluate two strategies: averaging mel-spectrograms across channels and summing patch-embedded representations. Our multi-microphone model achieves superior performance compared to single-channel baselines when tested on real-world reverberant environments.

Localization of acoustic sources has attracted a considerable amount of research attention in recent years. A major obstacle to achieving high localization accuracy is the presence of reverberation, the influence of which obviously increases with the number of active speakers in the room. Human hearing is capable of localizing acoustic sources even in extreme conditions. In this study, we propose to combine a method based on human hearing mechanisms and a modified incremental distributed expectation-maximization (IDEM) algorithm. Rather than using phase difference measurements that are modeled by a mixture of complex-valued Gaussians, as proposed in the original IDEM framework, we propose to use time difference of arrival measurements in multiple subbands and model them by a mixture of real-valued truncated Gaussians. Moreover, we propose to first filter the measurements in order to reduce the effect of the multipath conditions. The proposed method is evaluated using both simulated data and real-life recordings.

Non-covalent residue-residue contacts drive the folding of proteins and stabilize them. They may be local-i.e. involve residues that are close in sequence, or non-local. It has been suggested that, in most proteins, local contacts drive protein folding by providing crucial constraints of the conformational space, thus allowing proteins to fold. We compared residues that are involved in local contacts to residues that are involved in non-local contacts and found that, in most proteins, residues in non-local contacts are significantly more conserved evolutionarily than residues in local contacts. Moreover, non-local contacts are more structurally conserved: a contact between positions that are distant in sequence is more likely to exist in many structural homologues compared with a contact between positions that are close in sequence. These results provide new insights into the mechanisms of protein folding and may allow for better prediction of critical intra-chain contacts.