AffectMachine-Classical: a novel system for generating affective classical music.

This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal (R2 = 0.96) to listeners, and is also quite convincing in terms of Valence (R2 = 0.90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional wellbeing in listeners.

MERP: A Music Dataset with Emotion Ratings and Raters' Profile Information.

Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence-arousal space. Four additional songs were selected from the DEAM dataset to act as a benchmark in this study and filter out low quality ratings. A total of 452 participants participated in annotating the dataset, with 277 participants remaining after thoroughly cleaning the dataset. Their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.

Music Therapy During COVID-19: Changes to the Practice, Use of Technology, and What to Carry Forward in the Future.

In recent years, the field of music therapy (MT) has increasingly embraced the use of technology for conducting therapy sessions and enhancing patient outcomes. Amidst a worldwide pandemic, we sought to examine whether this is now true to an even greater extent, as many music therapists have had to approach and conduct their work differently. The purpose of this survey study is to observe trends in how music therapists from different regions around the world have had to alter their practice, especially in relation to their use of technology during the COVID-19 pandemic, because of limited options to conduct in-person therapy due to social distancing measures. Further, the findings aim to clarify music therapists' perspectives on the benefits and limitations of technology in MT, as well as online MT. In addition, this survey investigated what changes have been necessary to administer MT during COVID-19, in terms of virtual therapy and online tools, and how the changes made now may affect MT in the future. We also explored music therapists' views on whether special technology-focused training might be helpful to support the practice of MT in the future. This is the first survey, to our knowledge, to break down opinions of and trends in technology use based on geographical region (North America, Europe, and Asia), and several noteworthy differences were apparent across regions. We hope our findings provide useful information, guidance, and a global reference point for music therapists on effectively continuing the practice of MT during times of crisis, and can encourage reflection and improvement in administering MT.

Age-dependent statistical learning trajectories reveal differences in information weighting.

Statistical learning (SL) is the ability to generate predictions based on probabilistic dependencies in the environment, an ability that is present throughout life. The effect of aging on SL is still unclear. Here, we explore statistical learning in healthy adults (40 younger and 40 older). The novel paradigm tracks learning trajectories and shows age-related differences in overall performance, yet similarities in learning rates. Bayesian models reveal further differences between younger and older adults in dealing with uncertainty in this probabilistic SL task. We test computational models of 3 different learning strategies: (a) Win-Stay, Lose-Shift, (b) Delta Rule Learning, (c) Information Weights to explore whether they capture age-related differences in performance and learning in the present task. A likely candidate mechanism emerges in the form of age-dependent differences in information weights, in which young adults more readily change their behavior, but also show disproportionally strong reactions toward erroneous predictions. With lower but more balanced information weights, older adults show slower behavioral adaptation but eventually arrive at more stable and accurate representations of the underlying transitional probability matrix. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A closed-loop, music-based brain-computer interface for emotion mediation.

Emotions play a critical role in rational and intelligent behavior; a better fundamental knowledge of them is indispensable for understanding higher order brain function. We propose a non-invasive brain-computer interface (BCI) system to feedback a person's affective state such that a closed-loop interaction between the participant's brain responses and the musical stimuli is established. We realized this concept technically in a functional prototype of an algorithm that generates continuous and controllable patterns of synthesized affective music in real-time, which is embedded within a BCI architecture. We evaluated our concept in two separate studies. In the first study, we tested the efficacy of our music algorithm by measuring subjective affective responses from 11 participants. In a second pilot study, the algorithm was embedded in a real-time BCI architecture to investigate affective closed-loop interactions in 5 participants. Preliminary results suggested that participants were able to intentionally modulate the musical feedback by self-inducing emotions (e.g., by recalling memories), suggesting that the system was able not only to capture the listener's current affective state in real-time, but also potentially provide a tool for listeners to mediate their own emotions by interacting with music. The proposed concept offers a tool to study emotions in the loop, promising to cast a complementary light on emotion-related brain research, particularly in terms of clarifying the interactive, spatio-temporal dynamics underlying affective processing in the brain.