The behavior of users of music streaming services is investigated from the point of view of the temporal dimension of individual songs; specifically, the main object of the analysis is the point in time within a song at which users stop listening and start streaming another song ("skip"). The main contribution of this study is the ascertainment of a correlation between the distribution in time of skipping events and the musical structure of songs. It is also shown that such distribution is not only specific to the individual songs, but also independent of the cohort of users and, under stationary conditions, date of observation. Finally, user behavioral data is used to train a predictor of the musical structure of a song solely from its acoustic content; it is shown that the use of such data, available in large quantities to music streaming services, yields significant improvements in accuracy over the customary fashion of training this class of algorithms, in which only smaller amounts of hand-labeled data are available.
Newer approaches, based on more recent Machine Learning techniques, include @cite_7 and @cite_12 , that achieve State of the Art accuracy in prediction by making use of a Convolutional Neural Network architecture.
@cite_12 @cite_7
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The determination of structural boundaries is a key task for understanding the structure of a musical piece, but it is also highly ambiguous. Recently, Convolutional Neural Networks (CNN) trained on spectrogram features and human annotations have been successfully used to tackle the problem, but still fall clearly behind human performance. We expand on the CNN approach by combining spectrograms with self-similarity lag matrices as audio features, thereby capturing more facets of the underlying structural information. Furthermore, in order to consider the hierarchical nature of structural organization, we explore different strategies to learn from the two-level annotations of main and secondary boundaries available in the SALAMI structural annotation dataset. We show that both measures improve boundary recognition performance, resulting in a significant improvement over the previous state of the art. As a side-effect, our algorithm can predict boundaries on two different structural levels, equivalent to the training data. The recognition of boundaries, e.g., between chorus and verse, is an important task in music structure analysis. The goal is to automatically detect such boundaries in audio signals so that the results are close to human annotation. In this work, we apply Convolutional Neural Networks to the task, trained directly on mel-scaled magnitude spectrograms. On a representative subset of the SALAMI structural annotation dataset, our method outperforms current techniques in terms of boundary retrievalF -measure at different temporal tolerances: We advance the state-of-the-art from 0:33 to 0:46 for tolerances of0:5 seconds, and from 0:52 to 0:62 for tolerances of3 seconds. As the algorithm is trained on annotated audio data without the need of expert knowledge, we expect it to be easily adaptable to changed annotation guidelines and also to related tasks such as the detection of song transitions.