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Music has long served as a bridge between science and nature, allowing for the artistic expression of natural and human‐made phenomena. While music has been used in geomorphology as an engaging teaching strategy, its application in specific scientific inquiries within geomorphology remains relatively unexplored. Drawing on the morphological similarities between music and terrain relief, this work introduced a novel music‐based method for modelling and expressing terrain relief based on the drainage basin profile (DBP). It converts terrain relief into pitches and time values according to mapping rules and then describes terrain relief in an audible form. Based on 5 sample areas and 360 drainage basins on the Loess Plateau, we developed the application of the proposed method on four core geomorphic tasks, including landform interpretation, analysis, recognition and classification. Experimental results show that (1) terrain music can interpret the terrain relief and landform evolution processes through its musical structure and rhythmic variations; (2) music derivatives are related to different terrain features, such as terrain relief, terrain variation intensity, landform evolution degree and terrain complexity, and have a well‐functioning relationship with a series of conventional terrain derivatives; (3) leveraging machine learning techniques, the terrain music method is effective for landform recognition, achieving an overall accuracy of 88.85% and a mean accuracy of 88.85%; and (4) via a case study in Northern Shaanxi, music modelling successfully divided it into 12 distinct landform regions and 8 landform types. Different landform regions exhibit clear regional boundaries and gradual transition zones, while specific landform regions share prominent terrain, spatial clustering and landform processes. Our delineation provides reasonable and effective landform differentiation but captures additional bed‐rock mountain features compared with a traditional method. This study highlights that music is not only an artistic expression but also a valuable research paradigm for a wide range of geomorphic tasks, offering fresh perspectives and enhancing our understanding of loess landforms. The results show a promising effort to integrate music theory into practical geomorphic tasks, demonstrating the potential of using music as a medium for conveying and analysing spatial information in geomorphology. [ABSTRACT FROM AUTHOR]

David Temperley's book, "The Musical Language of Rock," explores the conventions and language of rock music. Temperley argues that experienced rock listeners unconsciously understand these conventions and can detect deviations from them. The book combines theory and analysis, using musical examples to develop theoretical models. Temperley examines various aspects of rock music, including harmony, emotion, complexity, melody, rhythm, and form. He also considers the historical development of rock and its formal functions. The book offers new insights and perspectives on the musical language of rock. [Extracted from the article]

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses. [ABSTRACT FROM AUTHOR]