Semantic clustering of bioacoustic signals is crucial for a deeper understanding of intra-class differences. This is particularly important for understanding killer whale signals, as their vocalizations are learned behaviors and determination of matrilineal-specific dialects is reliant upon subtle differences within instances which may be characterized into a single larger category. Aspects of data collection may have an effect on how these calls are grouped, and it is therefore necessary to understand what the focus of the feature generation algorithm is. This study addresses the impact of factors such as recording conditions and environment, together with its respective relative noise levels, by first analyzing two different deep learning and data-driven feature representations, either derived by an undercomplete autoencoder or a supervised call type classifier. These are then compared with representations generated by two state-of-the-art transformer-based tools, namely HuBERT and Wav2Vec2.

The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the Emotion Share Sub-Challenge, a regression on speech has to be made; and in the Requests Sub-Challenges, requests and complaints need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComPaRE features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectRum toolkit; in addition, wav2vec2 models are used.