In a jazz ensemble, the timing patterns within each instrument and between instruments vary systematically depending on the instrument, tempo, style, and other parameters. A set of computer tools is described to modify these timing parameters according to previous measurements, allowing a large flexibility to account for individual differences and preferences. Four different jazz trio recordings were transcribed and annotated, and the tools were then used to recreate or modify the timing patterns in synthesized versions. These tools can be used for pedagogical purposes in which a music example can be played with different timing interpretations. It can also be used as a tool for research in which controlled factorial experiments can be designed.

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.

Sensory valuation is a fundamental aspect of cognition. It involves assigning hedonic value to a stimulus based on its sensory information considering personal and contextual factors. Hedonic values (e.g., liking) can be deemed affective states that motivate behavior, but the relations between hedonic and affective judgments have yet to be established. To fill this gap, we investigated the relations between stimulus features, perceived affect, and liking across domains and with potentially relevant individual traits. Fifty-eight participants untrained in music and visual art rated their liking and perceived valence and arousal for visual designs and short melodies varying in balance, contour, symmetry, or complexity and filled out several questionnaires. First, we examined group-level relations between perceived affect and liking across domains. Second, we inspected the relations between the individual use of musical and visual properties in judgments of liking and perceived affect-that is, between aesthetic and perceived-affect sensitivities. Third, we inquired into the influence of information-related (need for cognition, or NFC) and affect-related (need for emotion) traits on individual sensitivities. We found domain-specific effects of the stimulus features on liking, a linear association between valence and liking, the inverted-U model of arousal and liking, a binary profile of musical aesthetic sensitivities, and a modulatory effect of NFC on how people use stimulus properties in their hedonic and affective judgments. In summary, the results suggest that hedonic value is primarily computed from domain-specific sensory information partially moderated by NFC. 

The insula contributes to the detection of salient events during goal-directed behavior and participates in the coordination of motor, multisensory, and cognitive systems. Recent task-fMRI studies with trained singers sug-gest that singing experience can enhance the access to these resources. However, the long-term effects of vocal training on insula-based networks are still unknown. In this study, we employed resting-state fMRI to assess experience-dependent differences in insula co-activation patterns between conservatory-trained singers and non-singers. Results indicate enhanced bilateral anterior insula connectivity in singers relative to non-singers with constituents of the speech sensorimotor network. Specifically, with the cerebellum (lobule V-VI) and the superior parietal lobes. The reversed comparison showed no effects. The amount of accumulated singing training pre-dicted enhanced bilateral insula co-activation with primary sensorimotor areas representing the diaphragm and the larynx/phonation area-crucial regions for cortico-motor control of complex vocalizations-as well as the bilateral thalamus and the left putamen. Together, these findings highlight the neuroplastic effect of expert singing training on insula-based networks, as evidenced by the association between enhanced insula co-activation profiles in singers and the brain's speech motor system components.

This volume presents the proceedings of the fifth Stockholm Music Acoustics Conference 2023 (SMAC), which took place on 14–15 June 2023 in Stockholm, Sweden. SMAC was premiered at KTH in 1983, and has been organized every tenth year since then. This conference is intended for academics, music performers and instructors interested in the field of Music Acoustics. It brings together experts from different disciplines, to exchange and share their recent works on many aspects of Music Acoustics, including instrument acoustics, singing voice acoustics, acoustics-based synthesis models, music performance, and music acoustics in teaching and pedagogy.

This time, our multidisciplinary conference was organized on a smaller scale than earlier, as a track within the 2023 Sound and Music Computing Conference, at KMH Royal College of Music and KTH Royal Institute of Technology. Our warm thanks are due to the SMC Network for hosting SMAC in the framework of SMC, as are many thanks to all presenters and co-authors for participating. We hope that you will enjoy learning of the new science presented here.

Sara D’Amario, Sten Ternström and Anders Friberg

Track chairs, Editors

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.

The primary purpose of this paper is to describe a method to investigate the communication process between musicians performing improvisation in jazz. This method was applied in a first case study. The paper contributes to jazz improvisation theory towards embracing more artistic expressions and choices made in real life musical situations. In jazz, applied improvisation theory usually consists of scale and harmony studies within quantized rhythmic patterns. The ensembles in the study were duos performed by the author at the piano and horn players (trumpet, alto saxophone, clarinet and trombone). Recording sessions involving the ensembles were conducted. The recording was transcribed using software and the produced score together with the audio recording was used when conducting in-depth interviews, to identify the horn player’s underlying musical strategies. The strategies were coded according to previous research.

While the significance of auditory cortical regions for the development and maintenance of speech motor coordination is well established, the contribution of somatosensory brain areas to learned vocalizations such as singing is less well understood. To address these mechanisms, we applied intermittent theta burst stimulation (iTBS), a facilitatory repetitive transcranial magnetic stimulation (rTMS) protocol, over right somatosensory larynx cortex (S1) and a nonvocal dorsal S1 control area in participants without singing experience. A pitch‐matching singing task was performed before and after iTBS to assess corresponding effects on vocal pitch regulation. When participants could monitor auditory feedback from their own voice during singing (Experiment I), no difference in pitch‐matching performance was found between iTBS sessions. However, when auditory feedback was masked with noise (Experiment II), only larynx‐S1 iTBS enhanced pitch accuracy (50–250 ms after sound onset) and pitch stability (>250 ms after sound onset until the end). Results indicate that somatosensory feedback plays a dominant role in vocal pitch regulation when acoustic feedback is masked. The acoustic changes moreover suggest that right larynx‐S1 stimulation affected the preparation and involuntary regulation of vocal pitch accuracy, and that kinesthetic‐proprioceptive processes play a role in the voluntary control of pitch stability in nonsingers. Together, these data provide evidence for a causal involvement of right larynx‐S1 in vocal pitch regulation during singing.

This paper presents a convolutional neural network (CNN) that uses input from a polyphonic pitch estimation system to predict perceived minor/major modality in music audio. The pitch activation input is structured to allow the first CNN layer to compute two pitch chromas focused on dif-ferent octaves. The following layers perform harmony analysis across chroma and time scales. Through max pooling across pitch, the CNN becomes invariant with re-gards to the key class (i.e., key disregarding mode) of the music. A multilayer perceptron combines the modality ac-tivation output with spectral features for the final predic-tion. The study uses a dataset of 203 excerpts rated by around 20 listeners each, a small challenging data size re-quiring a carefully designed parameter sharing. With an R2 of about 0.71, the system clearly outperforms previous sys-tems as well as individual human listeners. A final ablation study highlights the importance of using pitch activations processed across longer time scales, and using pooling to facilitate invariance with regards to the key class.

This paper presents a convolutional neural network (CNN) able to predict the perceived dissonance of piano chords. Ratings of dissonance for short audio excerpts were com- bined from two different datasets and groups of listeners. The CNN uses two branches in a directed acyclic graph (DAG). The first branch receives input from a pitch esti- mation algorithm, restructured into a pitch chroma. The second branch analyses interactions between close partials, known to affect our perception of dissonance and rough- ness. The analysis is pitch invariant in both branches, fa- cilitated by convolution across log-frequency and octave- wide max-pooling. Ensemble learning was used to im- prove the accuracy of the predictions. The coefficient of determination (R2) between rating and predictions are close to 0.7 in a cross-validation test of the combined dataset. The system significantly outperforms recent computational models. An ablation study tested the impact of the pitch chroma and partial analysis branches separately, conclud- ing that the deep layered learning approach with a pitch chroma was driving the high performance.