@article {2024, title = {New advances and novel applications of music technologies for health, well-being, and inclusion Front. Comput. Sci. 6:1358454. doi: 10.3389/fcomp.2024.1358454}, journal = {Frontiers in Computer Science}, volume = {6:1358454}, year = {2024}, month = {01/2024}, keywords = {accessibility, Digital Musical Instruments, healthcare, multimodal interaction, music technology, Music Therapy, sound and music, wellbeing}, doi = {doi:10.3389/fcomp.2024.1358454}, author = {Emma Frid and Kjetil Falkenberg and Kat Agres and Alex Lucas} } @article {2023, title = {Intra- and inter-brain coupling and activity dynamics during improvisational music therapy with a person with dementia: an explorative EEG-hyperscanning single case study}, journal = {Frontiers in Psychology: Cognitive Science}, volume = {14:1155732}, year = {2023}, month = {09/2023}, abstract = {Objective: Real-life research into the underlying neural dynamics of improvisational music therapy, used with various clinical populations, is largely lacking. This single case study explored within-session differences in musical features and in within- and between-brain coupling between a Person with Dementia (PwD) and a music therapist during a music therapy session. Methods: Dual-EEG from a music therapist and a PwD (male, 31 years) was recorded. Note density, pulse clarity and synchronicity were extracted from audio-visual data. Three music therapists identified moments of interest and no interest (MOI/MONI) in two drum improvisations. The Integrative Coupling Index, reflecting time-lagged neural synchronization, and musical features were compared between the MOI and MONI. Results: Between-brain coupling of 2 Hz activity was increased during the MOI, showing anteriority of the therapist{\textquoteright}s neural activity. Within-brain coupling for the PwD was stronger from frontal and central areas during the MOI, but within-brain coupling for the therapist was stronger during MONI. Differences in musical features indicated that both acted musically more similar to one another during the MOI. Conclusion: Within-session differences in neural synchronization and musical features highlight the dynamic nature of music therapy. Significance: The findings contribute to a better understanding of social and affective processes in the brain and (interactive) musical behaviors during specific moments in a real-life music therapy session. This may provide insights into the role of such moments for relational-therapeutic processes.}, keywords = {brain coupling, dementia, EEG, improv, Music Therapy}, doi = {doi:10.3389/fpsyg.2023.1155732}, author = {Clemens Maidhof and Viktor M{\"u}ller and Olivier Lartillot and Kat Agres and Jodie Bloska and Rie Asano and Helen Odell-Miller and J{\"o}rg Fachner} } @conference {2020, title = {Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders}, booktitle = {45th International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2020)}, year = {2020}, abstract = {We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion. The proposed model is trained on non-parallel corpora, accommodates many-to-many conversion, and leverages recent advances of variational autoencoders. It employs separate encoders to learn disentangled latent representations of singer identity and vocal technique separately, with a joint decoder for reconstruction. Conversion is carried out by simple vector arithmetic in the learned latent spaces. Both a quantitative analysis as well as a visualization of the converted spectrograms show that our model is able to disentangle singer identity and vocal technique and successfully perform conversion of these attributes. To the best of our knowledge, this is the first work to jointly tackle conversion of singer identity and vocal technique based on a deep learning approach.}, author = {Yin-Jyun Luo and Chin-Cheng Hsu and Kat Agres and Dorien Herremans} } @conference {2019, title = {Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders}, booktitle = {ISMIR}, year = {2019}, publisher = {ISMIR}, organization = {ISMIR}, address = {Delft, The Netherlands}, url = {https://arxiv.org/abs/1906.08152}, author = {Y.J. Luo and K. Agres and D. Herremans} } @conference {2019, title = {A novel music-based game with motion capture to support cognitive and motor function in the elderly}, booktitle = {IEEE Conference on Games (CoG) 2019}, year = {2019}, month = {08/2019}, abstract = {This paper presents a novel game prototype that uses music and motion detection as preventive medicine for the elderly. Given the aging populations around the globe, and the limited resources and staff able to care for these populations, eHealth solutions are becoming increasingly important, if not crucial, additions to modern healthcare and preventive medicine. Furthermore, because compliance rates for performing physical exercises are often quite low in the elderly, systems able to motivate and engage this population are a necessity. Our prototype uses music not only to engage listeners, but also to leverage the efficacy of music to improve mental and physical wellness. The game is based on a memory task to stimulate cognitive function, and requires users to perform physical gestures to mimic the playing of different musical instruments. To this end, the Microsoft Kinect sensor is used together with a newly developed gesture detection module in order to process users{\textquoteright} gestures. The resulting prototype system supports both cognitive functioning and physical strengthening in the elderly.}, keywords = {aging population, cognitive function, eHealth, motion detection, motor skills, music, serious games, telehealth}, author = {Agres, K and Lui, S and Herremans, D} } @article {2019, title = {Statistical Learning Ability as a Measure of Cognitive Function}, year = {2019}, month = {07/2019}, pages = {1-7}, publisher = {PsyArXiv}, type = {Archive}, keywords = {Auditory Domain, Cognitive Assessment, Continuous Paradigm, Elderly, Statistical Learning}, doi = {doi.org/10.31234/osf.io/u4ry6}, author = {Herff, S and Rashid, N.A and Lee, J and Lee, T.S and Agres, K} } @conference {33, title = {Co-occurrences of harmonics bootstrap pitch and tonality perception in music: Evidence from a statistical unsupervised learning model}, booktitle = {CogSci 2015: The annual meeting of the Cognitive Science Society}, year = {2015}, address = {Pasadena, CA, USA}, author = {K. Agres and C. E. Cancino Chac{\'o}n and M. Grachten and S. Lattner} } @inbook {30, title = {Probabilistic Segmentation of Musical Sequences using Restricted Boltzmann Machines}, booktitle = {Mathematics and Computation in Music}, volume = {9110}, number = {Lecture Notes in Computer Science}, year = {2015}, pages = {323-334}, publisher = {Springer}, organization = {Springer}, issn = {978-3-319-20602-8}, author = {Lattner, Stefan and Grachten, Maarten and Agres, Kat and Chac{\'o}n, Carlos} }