Analysts of twelve‐note music tend to treat this repertoire as if it were composed of numbers rather than notes. The first part of this article asks how they might treat this music as if it were music. A theoretical position is developed via close readings of the work a number of theorists and philosophers, notably Roger Scruton and Theodor W. Adorno. In Part II, the potential of the position developed in Part I is tested in a review of three books on twelve‐note music by Nikos Skalkottas and Luigi Dallapiccola.

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Drawing on the tradition of Formenlehre, this article puts forward a methodological historicism as a means of mediating between the disciplinary expectations of musical analysis, on the one hand, and philosophical aesthetics, on the other. Stylistic developments in the later music of Frank Bridge, perhaps British music's best claim to a high modernist of the generation of Schoenberg and Stravinsky, are illuminated by means of Theodor W. Adorno's notion of musical ‘reification’. A comparative analysis of the complementary modernism of Bridge's contemporary Ralph Vaughan Williams is also put forward, and a critical light shone on recent writing on British musical modernism in general.

The Visual-and-Language Navigation (VLN) task requires understanding a textual instruction to navigate a natural indoor environment using only visual information. While this is a trivial task for most humans, it is still an open problem for AI models. In this work, we hypothesize that poor use of the visual information available is at the core of the low performance of current models. To support this hypothesis, we provide experimental evidence showing that state-of-the-art models are not severely affected when they receive just limited or even no visual data, indicating a strong overfitting to the textual instructions. To encourage a more suitable use of the visual information, we propose a new data augmentation method that fosters the inclusion of more explicit visual information in the generation of textual navigational instructions. Our main intuition is that current VLN datasets include textual instructions that are intended to inform an expert navigator, such as a human, but not a beginner visual navigational agent, such as a randomly initialized DL model. Specifically, to bridge the visual semantic gap of current VLN datasets, we take advantage of metadata available for the Matter-port3D dataset that, among others, includes information about object labels that are present in the scenes. Training a state-of-the-art model with the new set of instructions increase its performance by 8% in terms of success rate on unseen environments, demonstrating the advantages of the proposed data augmentation method.

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