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.

Data-driven and machine-based analysis of massive bioacoustic data collections, in particular acoustic regions containing a substantial number of vocalizations events, is essential and extremely valuable to identify recurring vocal paradigms. However, these acoustic sections are usually characterized by a strong incidence of overlapping vocalization events, a major problem severely affecting subsequent human-/machine-based analysis and interpretation. Robust machine-driven signal separation of species-specific call types is extremely challenging due to missing ground truth data, speaker/source-relevant information, limited knowledge about inter- and intra-call type variations, next to diverse recording conditions. The current study is the first introducing a fully-automated deep signal separation approach for overlapping orca vocalizations, addressing all of the previously mentioned challenges, together with one of the largest bioacoustic data archives recorded on killer whales (Orcinus Orca). Incorporating ORCA-PARTY as additional data enhancement step for downstream call type classification demonstrated to be extremely valuable. Besides the proof of cross-domain applicability and consistently promising results on non-overlapping signals, significant improvements were achieved when processing acoustic orca segments comprising a multitude of vocal activities. Apart from auspicious visual inspections, a final numerical evaluation on an unseen dataset proved that about 30 % more known sound patterns could be identified.

The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComParE and BoAW features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectrum toolkit; in addition, we add end-to-end sequential modelling, and a log-mel-128-BNN.

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AuDeep toolkit, and deep feature extraction from pre-trained CNNs using the Deep Spectrum toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.

In bioacoustics, passive acoustic monitoring of animals living in the wild, both on land and underwater, leads to large data archives characterized by a strong imbalance between recorded animal sounds and ambient noises. Bioacoustic datasets suffer extremely from such large noise-variety, caused by a multitude of external influences and changing environmental conditions over years. This leads to significant deficiencies/problems concerning the analysis and interpretation of animal vocalizations by biologists and machine-learning algorithms. To counteract such huge noise diversity, it is essential to develop a denoising procedure enabling automated, efficient, and robust data enhancement. However, a fundamental problem is the lack of clean/denoised ground-truth samples. The current work is the first presenting a fully-automated deep denoising approach for bioacoustics, not requiring any clean ground-truth, together with one of the largest data archives recorded on killer whales (Orcinus Orca) – the Orchive. Therefor, an approach, originally developed for image restoration, known as Noise2Noise (N2N), was transferred to the field of bioacoustics, and extended by using automatic machine-generated binary masks as additional network attention mechanism. Besides a significant cross-domain signal enhancement, our previous results regarding supervised orca/noise segmentation and orca call type identification were outperformed by applying ORCACLEAN as additional data preprocessing/enhancement step

The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart.We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.

Audiovisual media are increasingly used to study the communication and behavior of animal groups, e.g. by placing microphones in the animals habitat resulting in huge datasets with only a small amount of animal interactions. The Orcalab has recorded orca whales since 1973 using stationary underwater hydrophones and made it publicly available on the Orchive. There exist over 15 000 manually extracted orca/noise annotations and about 20 000 h unseen audio data. To analyze the behavior and communication of killer whales we need to interpret the different call types. In this work, we present a two-stage classification approach using the labeled call/noise files and a few labeled call-type files. Results indicate a reliable accuracy of 95.0 % for call segmentation and 87 % for classification of 12 call classes. We further visualize the learned orca call representations in the convolutional neural network (CNN) activations to explain the potential of CNN based recognition for bioaccousitc signals.

Classical parametric speech coding techniques provide a compact representation for speech signals. This affords a very low transmission rate but with a reduced perceptual quality of the reconstructed signals. Recently, autoregressive deep generative models such as WaveNet and SampleRNN have been used as speech vocoders to scale up the perceptual quality of the reconstructed signals without increasing the coding rate. However, such models suffer from a very slow signal generation mechanism due to their sample-by-sample modelling approach. In this work, we introduce a new methodology for neural speech vocoding based on generative adversarial networks (GANs). A fake speech signal is generated from a very compressed representation of the glottal excitation using conditional GANs as a deep generative model. This fake speech is then refined using the LPC parameters of the original speech signal to obtain a natural reconstruction. The reconstructed speech waveforms based on this approach show a higher perceptual quality than the classical vocoder counterparts according to subjective and objective evaluation scores for a dataset of 30 male and female speakers. Moreover, the usage of GANs enables to generate signals in one-shot compared to autoregressive generative models. This makes GANs promising for exploration to implement high-quality neural vocoders.

The INTERSPEECH 2019 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Styrian Dialects Sub-Challenge, three types of Austrian-German dialects have to be classified; in the Continuous Sleepiness Sub-Challenge, the sleepiness of a speaker has to be assessed as regression problem; in the Baby Sound Sub-Challenge, five types of infant sounds have to be classified; and in the Orca Activity Sub-Challenge, orca sounds have to be detected.We describe the Sub-Challenges and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by the 'usual' ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit.