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In adults, specific neural systems with right-hemispheric weighting are necessary to process pitch, melody, and harmony as well as structure and meaning emerging from musical sequences. It is not known to what extent the specialization of these systems results from long-term exposure to music or from neurobiological constraints. One way to address this question is to examine how these systems function at birth, when auditory experience is minimal. We used functional MRI to measure brain activity in 1- to 3-day-old newborns while they heard excerpts of Western tonal music and altered versions of the same excerpts. Altered versions either included changes of the tonal key or were permanently dissonant. Music evoked predominantly right-hemispheric activations in primary and higher order auditory cortex. During presentation of the altered excerpts, hemodynamic responses were significantly reduced in the rig1ht auditory cortex, and activations emerged in the left inferior frontal cortex and limbic structures. These results demonstrate that the infant brain shows a hemispheric specialization in processing music as early as the first postnatal hours. Results also indicate that the neural architecture underlying music processing in newborns is sensitive to changes in tonal key as well as to differences in consonance and dissonance.

This article examines how consumers’ preferences are affected by the interplay between their level of arousal and the valence of their current affective state. Building on prior research examining the regulation of mood valence, the authors propose that consumers are also motivated to manage their level of arousal. It is predicted that this motivation systematically affects consumers’ product preferences such that consumers in a pleasant mood will tend to choose products that are congruent with their current level of arousal, while those in an unpleasant mood will tend to choose products that are incongruent with their current level of arousal. The results of three consequential choice studies—that use scent and music to vary consumers’ moods—provide strong support for the hypotheses. The article concludes with a discussion of the theoretical implications of the results.

This book builds on and in many ways completes the project of Fred Lerdahl and Ray Jackendoff's influential A Generative Theory of Tonal Music. Like the earlier volume, this book is both a music-theoretic treatise and a contribution to the cognitive science of music. After presenting some modifications to Lerdahl and Jackendoff's original framework, the book develops a quantitative model of listeners' intuitions of the relative distances of pitches, chords, and regions from a given tonic. The model is used to derive prolongational structure, trace paths through pitch space at multiple prolongational levels, and compute patterns of tonal tension and attraction as musical events unfold. The consideration of pitch-space paths illuminates issues of musical narrative, and the treatment of tonal tension and attraction provides a technical basis for studies of musical expectation and expression. These investigations lead to a fresh theory of tonal function and reveal an underlying parallel between tonal and metrical structures. Later portions of the book apply these ideas to highly chromatic tonal as well as atonal music. In response to stylistic differences, the shape of pitch space changes and psychoacoustic features become increasingly important, while underlying features of the theory remain constant, reflecting unvarying features of the musical mind. The theory is illustrated throughout by analyses of music from Bach to Schoenberg, and frequent connections are made to the music-theoretic and psychological literature.

A large-scale study is reported whose purpose was to elucidate the historical development of the European major and minor modes. The study involved 455 musical works by 259 composers sampled across the years 1400 to 1750. Beginning with the period 1700-1750, a series of statistical studies was carried out on the distribution of scale tones, progressively moving backward in time. The method utilized a modified method of key determination – generalized to handle an arbitrary number of modal classifications. The results from cluster analyses on this data are consistent with the view that the modern major and minor modes have changed over time and were preceded by a system in which there were more than just two modes.

The Role of Anticipatory Auditory Imagery inmusical ensemble performance was investigated by examining the relationship between individual differences in auditory imagery and temporal coordination in piano duos. Vividness of imagery for upcoming sounds was assessed in 14 pianists using a task that required the production of rhythmic sequences with or without auditory feedback. Ensemble coordination was assessed by examining temporal relations between body movements (recorded by a motion capture system) and sound onsets (triggered by key strokes on two MIDI pianos) in seven duos playing two contrasting pieces with or without visual contact. Sound synchrony was found to be related to anterior-posterior body sway coordination in a manner that depended upon leader/follower relations between pianists assigned to 'primo' and 'secondo' parts. Furthermore, the quality of coordination, which was not affected markedly by whether pianists were in visual contact, was correlated with individual differences in anticipatory auditory imagery. These findings suggest that auditory imagery facilitates interpersonal coordination by enhancing the operation of internal models that simulate one's own and others' actions during ensemble performance.

Computational models of key estimation have struggled to emulate the accuracy levels of human listeners, especially with pieces in the minor mode. The current study proposes a new key-finding algorithm, which utilizes Euclidean distance, rather than correlation, and is trained on the statistical properties of a large musical sample. A model was trained on a dataset of 490 pieces encoded into the Humdrum “kern” format, in which the key was known. This model was tested on a reserve dataset of 492 pieces, and was found to have a significantly higher overall accuracy than previous models. In addition, we determined separate accuracy ratings for major mode and minor mode works for the existing key-finding models and report that most existing models provide greater accuracy for major mode rather than minor mode works. The proposed key-finding algorithm performs more accurately on minor mode works than all of the other models tested, although it does not perform significantly better than the models created by Aarden (2003), Bellman (2005), or Sapp (2011). Finally, an algorithm that combines the Aarden-Essen model (2003) and the proposed algorithm is suggested, and results in significantly more accurate key assessments than all of the other extant models.

This paper undertakes a comparative study of concepts and visual representations of hierarchical aspects of musical structure. After consideration of the rhythmic components of grouping and meter, the discussion turns to pitch-event hierarchies and the tonal hierarchy (or pitch space). Contrasting notations are evaluated in terms of the efficacy of the concepts they exemplify.

Every piece of music is characterized by its key, melody, harmony, metre, and rhythm. Musical information retrieval tasks like transcription, chord estimation, and harmony analysis require musical key data as the fundamental comprehension for their implementation. Even though several investigations were carried out by researchers aimed at developing an optimum key profile for a given melody, the possibilities of finding the key using machine learning techniques have been least explored. In this paper, we present a novel approach to determine the musical key of a given song. The proposed model features a simple architecture for learning and classification. It was tested with four distinct machine learning algorithms namely K-nearest neighbor (KNN), Naïve Bayes (NB), Discriminant Analysis (DA), and Support Vector Machine (SVM). In addition, a dataset of different genres of music has been compiled, for our experiments. The Pitch Class Profile (PCP) distribution of our dataset has been compared with renowned datasets and it showed similar distribution with the others. We optimized our model with the best classifier from all the four machine learning techniques we used. Out of the four machine learning algorithms used in our model, the SVM gave an accuracy value of 91.49% with the highest precision and recall values. The KNN approach showed an accuracy of 89.76% followed by Naïve Bayes and the Discriminant Analysis classifiers with an accuracy of 87.11% and 86.77% respectively. Also, the error rates of these different approaches ranged from 8.51% to 13.23%. These results show that the proposed model with SVM algorithm has a considerably higher accuracy value, and in comparison with recent publications, it is evident that our model can play a pivotal role in the efficient determination of keys since it brings together information related to musical theory and supervised learning techniques for classification.