Probing the underlying principles of dynamics in piano performances using a modelling approach

Variations in dynamics are an essential component of musical performance in most instruments. To study the factors that contribute to dynamic variations, we used a model approaching, allowing for determination of the individual contribution of different musical features. Thirty monophonic melodies from 3 stylistic eras with all expressive markings removed were performed by 20 pianists on a Disklavier piano. The results indicated a relatively high agreement among the pianists (Cronbach's alpha = 0.88). The overall average dynamics (across pianists) could be predicted quite well using support vector regression (R2 = 66%) from a set of 48 score-related features. The highest contribution was from pitch-related features (37.3%), followed by phrasing (12.3%), timing (2.8%), and meter (0.7%). The highest single contribution was from the high-loud principle, whereby higher notes were played louder, as corroborated by the written feedback of many of the pianists. There were also differences between the styles. The highest contribution from phrasing, for example, was obtained from the Romantic examples, while the highest contribution from meter came from the Baroque examples. An analysis of each individual pianist revealed some fundamental differences in approach to the performance of dynamics. All participants were undergraduate-standard pianists or above; however, varied levels of consistency and predictability highlighted challenges in acquiring a reliable group in terms of expertise and preparation, as well as certain pianistic challenges posed by the task. Nevertheless, the method proved useful in disentangling some underlying principles of musical performance and their relation to structural features of the score, with the potential for productive adaptation to a wider range of expressive and instrumental contexts.

Long Short-Term Memory-Based Music Analysis System for Music Therapy

Music can express people's thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of "diagnosis, treatment and evaluation." It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths for the development of the medical field. This paper proposes an long short-term memory (LSTM)-based generation and classification algorithm for multi-voice music data. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. MVMG contains two main steps. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model.

Igbo Speech Surrogacy: Preliminary Findings Based on the Oja Flute

This research report presents analyses of recordings from the Ìgbò culture of southeastern Nigeria of an ọ̀jà flute player, a female speaker, and a male speaker. After a prepared performance, the participants completed two tasks: (1) mapping speech to flute playing and (2) identifying phrases played on the flute. Contour analysis is applied to annotated recordings to study the mapping of speech tone and rhythm from voice to instrument in parallel utterances by the three participants (male, female, and flute). Response time between the flute playing and spoken phrase identification indicates each prompt's relative clarity. Using a limited but not predetermined inventory of related praise epithets, participants successfully converted speech to music and music to speech. In the conversion of speech to music, we found that declination was not part of the mapping, indicating it is a phonetic artifact of speech and does not carry a functional load. In identifying surrogate phrases played on the flute (music to speech), we found that dialectical variation caused some misidentification because idioms known in one area of the Igbo dialect cluster are not necessarily known throughout the region. However, òòjà speech surrogacy is found throughout the region. Possibilities and predictions for further research are presented.

Probing the Underlying Principles of Perceived Immanent Accents Using a Modeling Approach

This article deals with the question of how the perception of the “immanent accents” can be predicted and modeled. By immanent accent we mean any musical event in the score that is related to important points in the musical structure (e.g., tactus positions, melodic peaks) and is therefore able to capture the attention of a listener. Our aim was to investigate the underlying principles of these accented notes by combining quantitative modeling, music analysis and experimental methods. A listening experiment was conducted where 30 participants indicated perceived accented notes for 60 melodies, vocal and instrumental, selected from Baroque, Romantic and Post-tonal styles. This produced a large and unique collection of perceptual data about the perceived immanent accents, organized by styles consisting of vocal and instrumental melodies within Western art music. The music analysis of the indicated accents provided a preliminary list of musical features that could be identified as possible reasons for the raters’ perception of the immanent accents. These features related to the score in different ways, e.g., repeated fragments, single notes, or overall structure. A modeling approach was used to quantify the influence of feature groups related to pitch contour, tempo, timing, simple phrasing, and meter. A set of 43 computational features was defined from the music analysis and previous studies and extracted from the score representation. The mean ratings of the participants were predicted using multiple linear regression and support vector regression. The latter method (using cross-validation) obtained the best result of about 66% explained variance (r = 0.81) across all melodies and for a selected group of raters. The independent contribution of each feature group was relatively high for pitch contour and timing (9.6 and 7.0%). There were also significant contributions from tempo (4.5%), simple phrasing (4.4%), and meter (3.9%). Interestingly, the independent contribution varied greatly across participants, implying different listener strategies, and also some variability across different styles. The large differences among listeners emphasize the importance of considering the individual listener’s perception in future research in music perception.

A Computational Model of Immanent Accent Salience in Tonal Music

Accents are local musical events that attract the attention of the listener, and can be either immanent (evident from the score) or performed (added by the performer). Immanent accents involve temporal grouping (phrasing), meter, melody, and harmony; performed accents involve changes in timing, dynamics, articulation, and timbre. In the past, grouping, metrical and melodic accents were investigated in the context of expressive music performance. We present a novel computational model of immanent accent salience in tonal music that automatically predicts the positions and saliences of metrical, melodic and harmonic accents. The model extends previous research by improving on preliminary formulations of metrical and melodic accents and introducing a new model for harmonic accents that combines harmonic dissonance and harmonic surprise. In an analysis-by-synthesis approach, model predictions were compared with data from two experiments, respectively involving 239 sonorities and 638 sonorities, and 16 musicians and 5 experts in music theory. Average pair-wise correlations between raters were lower for metrical (0.27) and melodic accents (0.37) than for harmonic accents (0.49). In both experiments, when combining all the raters into a single measure expressing their consensus, correlations between ratings and model predictions ranged from 0.43 to 0.62. When different accent categories of accents were combined together, correlations were higher than for separate categories (r = 0.66). This suggests that raters might use strategies different from individual metrical, melodic or harmonic accent models to mark the musical events.