Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.

Sound classification algorithms are challenged by the natural variability of everyday sounds, particularly for large sound class taxonomies. In order to be applicable in real-life environments, such algorithms must also be able to handle polyphonic scenarios, where simultaneously occurring and overlapping sound events need to be classified. With the rapid progress of deep learning, several deep audio embeddings (DAEs) have been proposed as pre-trained feature representations for sound classification. In this article, we analyze the embedding spaces of two non-trainable audio representations (NTARs) and five DAEs for sound classification in polyphonic scenarios (sound event tagging) and make several contributions. First, we compare general properties like the inter-correlation between feature dimensions and the scattering of sound classes in the embedding spaces. Second, we test the robustness of the embeddings against several audio degradations and propose two sensitivity measures based on a class-agnostic and a class-centric view on the resulting drift in the embedding space. Finally, as a central contribution, we study how a blending between pairs of sounds maps to embedding space trajectories and how the path of these trajectories can cause classification errors due to their proximity to other sound classes. Throughout our analyses, the PANN embeddings have shown the best overall performance for low-polyphony sound event tagging.