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Labeling and maintaining a commercial sound effects library is a time-consuming task exacerbated by databases that continually grow in size and undergo taxonomy updates. Moreover, sound search and taxonomy creation are complicated by non-uniform metadata, an unrelenting problem even with the introduction of a new industry standard, the Universal Category System. To address these problems and overcome dataset-dependent limitations that inhibit the successful training of deep learning models, we pursue representation learning to train generalized embeddings that can be used for a wide variety of sound effects libraries and are a taxonomy-agnostic representation of sound. We show that a task-specific but dataset-independent representation can successfully address data issues such as class imbalance, inconsistent class labels, and insufficient dataset size, outperforming established representations such as OpenL3. Detailed experimental results show the impact of metric learning approaches and different cross-dataset training methods on representational effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a new benchmark task for generating musical passages in the audio domain by using the drum loops from the FreeSound Loop Dataset, which are publicly re-distributable. Moreover, we use a larger collection of drum loops from Looperman to establish four model-based objective metrics for evaluation, releasing these metrics as a library for quantifying and facilitating the progress of musical audio generation. Under this evaluation framework, we benchmark the performance of three recent deep generative adversarial network (GAN) models we customize to generate loops, including StyleGAN, StyleGAN2, and UNAGAN. We also report a subjective evaluation of these models. Our evaluation shows that the one based on StyleGAN2 performs the best in both objective and subjective metrics. [ABSTRACT FROM AUTHOR]

As the rapid development of multimedia networking. more and more songs are issued through the Internet and stored in large digital music libraries. However. music information retrieval on these libraries can be really hard. and the recognition of musical emotion is especially challenging. In tins paper. we report a strategy to recognize the emotion contained in songs by classifying their spectrograms. which contain both the time and frequency information, with a convolutional neural network (CNN). The experimente conducted on the 1000-song dataset indicate that the proposed model outperforms traditional machine learnng method. [ABSTRACT FROM AUTHOR]