@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 {2023, title = {Know Thyself: Improving interoceptive ability through ambient biofeedback in the workplace}, booktitle = {SIGHCI 2023 Proceedings}, year = {2023}, month = {12/2023}, publisher = {Association for Information Systems}, organization = {Association for Information Systems}, address = {Hyderabad, India}, abstract = {Interoception, the perception of the body{\textquoteright}s internal state, is intimately connected to self-regulation and wellbeing. Grounded in the affective science literature, we design an ambient biofeedback system called Soni-Phy and a lab study to investigate whether, when and how an unobtrusive biofeedback system can be used to improve interoceptive sensibility and accuracy by amplifying a users{\textquoteright} internal state. This research has practical significance for the design and improvement of assistive technologies for the workplace. }, keywords = {assistive augmentation, biofeedback, Interoception}, url = {https://aisel.aisnet.org/sighci2023/3}, author = {Phoebe Chua and Kat Agres and Suranga Nanayakkara} } @conference {2021, title = {Sen2Seq: A Conditional seq2seq Framework for Generating Lead Sheets with Sentiment}, booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Virtual}, author = {Dimos Makris and Kat Agres and D Herremans} } @article {9999, title = {nnAudio:~~An on-the-fly GPU Audio toSpectrogram Conversion Toolbox Using 1D Convolutional Neural Networks}, journal = {IEEE Access}, volume = {8}, year = {2020}, month = {09/2020}, pages = {161981-162003}, type = {Journal paper}, abstract = {In this paper, we present nnAudio, a new neural network based audio processing framework with graphics processing unit (GPU) support which leverages 1D convolutional neural networks to perform time domain to frequency domain conversion. It allows on-the-fly spectrogram extraction due to its fast speed, without the need to store any spectrograms on the disk. Moreover, this approach also allows back-propagation on the waveforms-to-spectrograms transformation layer, and hence, the transformation process can be made trainable, further optimizing the waveform-to-spectrogram transformation for the specific task that the neural network is trained on. All spectrogram implementations scale as Big-O of linear time with respect to the input length. nnAudio, however, leverages the compute unified device architecture (CUDA) of 1D convolutional neural network from PyTorch, its short-time Fourier transform (STFT), Mel spectrogram, and constant-Q transform (CQT) implementations are an order of magnitude faster than other implementations using only central processing unit (CPU). We tested our framework on three different machines with NVIDIA GPUs, and our framework significantly reduces the spectrogram extraction time from the order of seconds (using a popular python library librosa) to only the order of milliseconds when the audio recordings are of the same length. When applying nnAudio to variable input audio lengths, an average of 11.5 hours are required to extract 34 spectrogram types with different parameters from the MusicNet data set using librosa. An average of 2.8 hours is required for nnAudio, which is still four times faster than librosa. nnAudio also outperforms existing GPU processing libraries such as Kapre and torchaudio in terms of processing speed. }, doi = {doi: 10.1109/ACCESS.2020.3019084}, url = {https://ieeexplore.ieee.org/document/9174990}, author = {Kin Wai Cheuk and Hans Anderson and Kat Agres and Dorien Herremans} } @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} } @article {9999, title = {Change detection and schematic processing in music}, journal = {Psychology of Music}, volume = {47}, year = {2019}, pages = {173-193}, abstract = {Research in vision has highlighted the importance of gist representations in change detection and memory. This paper puts forth the hypothesis that schematic processing and gist provide an account for change detection in music as well, where a musical gist is an abstracted memory representation for schematically consistent tones. The present experiments illuminate the content of gist memory representations by testing when listeners can detect single-tone changes in pairs of melodies. In Experiment 1, musicians and non-musicians listened to melodies varying in tonal structure. Less structure resulted in compromised change detection in both groups. Most often, musicians displayed more accurate change detection than non-musicians, but surprisingly, when schematic processing could not contribute to memory encoding, musicians performed worse than their untrained counterparts. Experiment 2 utilized a full-factorial design to examine tonality, interval of pitch change, metrical position, and rhythm. Tonality had a particularly large effect on performance, with non-scale tones generally aiding change detection. Listeners were unlikely, however, to detect schematically-inconsistent tones when only brief melodic context was available. The results uphold the hypothesis that memory for melodies relies on schematic processing, with change detection dependent upon whether the change alters the schematic gist of the melody.}, doi = {DOI:10.1177/0305735617751249}, author = {Kat Agres} } @article {2019, title = {A closed-loop, music-based brain-computer interface for emotion mediation}, journal = {PLoS ONE}, volume = {14}, year = {2019}, month = {03/2019}, pages = {e0213516}, type = {Research Article}, abstract = {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{\textquoteright}s affective state such that a closed-loop interaction between the participant{\textquoteright}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{\textquoteright}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.}, keywords = {brain-computer interface, EEG, emotion mediation, music BCI, neurofeedback}, doi = {https://doi.org/10.1371/journal.pone.0213516}, author = {Stefan Ehrlich and Kat Agres and Cuntai Guan and Gordon Cheng} } @proceedings {2019, title = {A Comparative Study of Mental States in 2D and 3D Virtual Environments Using EEG}, year = {2019}, month = {10/2019}, publisher = {IEEE}, address = {Bari, Italy}, abstract = {There is growing evidence that virtual reality (VR) can be used as an optional therapy method for stress relief and treatment of mental disorders such as a variety of anxiety disorders, depression and psychosis. However, more systematic studies to quantify the effects of VR for emotion elicitation and compare the effects of 3D and 2D environment for the same is necessary to design feasible and effective treatments. In this study, we design a cross-over experiment protocol comprising of two emotion eliciting environments relaxation and arousal - that is presented to the participant either in a 2D monitor or a 3D head-mounted (HMD) VR display. The EEG data is collected during the experiment and analyzed offline to classify emotion elicited by low and high arousal environments using SVM classification of band power features. A 10-fold cross validation is performed and classification accuracy for each subject is computed for 3D-VR and 2D-screen display. The average classification accuracies over subjects are obtained as 66.88\% and 59.27\% in 3D-VR and 2D-screen group respectively. The performance difference between 3D-VR and 2D screen is statistically significant (p < 0.05), indicating that 3D-VR generates more distinct EEG patterns associated with emotion elicitation. Further, significant differences in band powers from alpha, theta and beta bands are also observed between both groups. The results presented in this paper support the use of VR as an effective tool to study emotion elicitation and regulation, compared to a 2D screen.}, author = {Pinar Bilgin and Kat Agres and Neethu Robinson and Aung Aung Phyo Wai and Cuntai Guan} } @inbook {2019, title = {The impact of musical structure on enjoyment and absorptive listening states in trance music}, booktitle = {Music and Consciousness 2: Worlds, Practices, Modalities}, volume = {Edited by Ruth Herbert, David Clarke, and Eric Clarke}, year = {2019}, publisher = {Oxford University Press}, organization = {Oxford University Press}, chapter = {14}, address = {Oxford}, keywords = {complexity, consciousness, enjoyment, Information theory, music structure, trance music}, issn = {ISBN: 9780198804352}, url = {https://books.google.com.sg/books?id=DCOQDwAAQBAJ\&pg=PA254\&lpg=PA254\&dq=The+impact+of+musical+structure+on+enjoyment+and+absorptive+listening+states+in+trance+music\&source=bl\&ots=FsWlNXsys-\&sig=ACfU3U0K6dFJD7zbC917zG6HRFXLxVSdvQ\&hl=en\&sa=X\&ved=2ahUKEwjd05}, author = {Kat Agres and Louis Bigo and Dorien Herremans} } @article {2019, title = {Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics}, journal = {Frontiers in Psychology: Cognitive Science}, volume = {10}, year = {2019}, month = {04/2019}, pages = {1-18}, type = {Original Research}, abstract = {In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process.}, keywords = {computational creativity, computational linguistics, conceptual models, distributional semantics, metaphor, vector space models}, doi = {doi: 10.3389/fpsyg.2019.00765}, author = {Stephen McGregor and Kat Agres and Karolina Rataj and Matthew Purver and Geraint Wiggins} } @proceedings {2018, title = {Modelling Novice and Expert Listeners{\textquoteright} Ability to Detect Changes in Short Melodies}, year = {2018}, address = {Sydney, Australia}, author = {Kat Agres and David Meredith} } @conference {2018, title = {The Structure of Chord Progressions Influences Listeners{\textquoteright} Enjoyment and Absorptive States in EDM}, booktitle = {International Conference on Music Perception and Cognition}, year = {2018}, address = {Sydney, Australia}, abstract = {The field of music cognition has given comparatively little consideration to the topic of altered listening states, such as audience flow, trancing, and absorptive states. Some research has investigated the relationship between musical features (such as repetitiveness or information-theoretic characteristics) and enjoyment of the music, but the impact of musical structure on absorptive states has yet to be thoroughly addressed. The present study sought to fill this gap in the literature by examining harmonic structure, enjoyment, and absorptive listening states in Uplifting Trance (UT), a sub-genre of Electronic Dance Music (EDM). Rather than examine the obvious (and previously considered) connection between driving rhythmic cues and absorptive listening states, we aimed to investigate the effect of harmonic repetition on listening states. Based on previous work (Agres, et al, 2017), we generated a number of UT excerpts with varying degrees of harmonic repetition. These excerpts were used in an online listening task with 19 experienced trance/EDM listeners. We discovered that chord progressions significantly influenced both listeners{\textquoteright} enjoyment of the music as well as their reported absorptive listening states. Overall, the results from this study indicate that moderately complex chord sequences elicit greater enjoyment than very repetitive chord sequences or sequences that violate listeners{\textquoteright} expectations. A significant correlation between highly enjoyed sequences and absorptive listening states was also observed.}, author = {Kat Agres and Dorien Herremans} } @proceedings {2017, title = {From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models}, year = {2017}, address = {Suzhou, China}, author = {Carlos Cancino-Chacon and Maarten Grachten and Kat Agres} } @proceedings {2017, title = {Intelligibility of Sung Lyrics: A Pilot Study}, year = {2017}, address = {Suzhou, China}, author = {Karim Ibrahim and David Grunberg and Kat Agres and Chitra Gupta and Ye Wang} } @article {9999, title = {Entraining IDyOT: timing in the information dynamics of thinking}, journal = {Frontiers in Psychology: Auditory Cognitive Neuroscience}, volume = {7:1575}, year = {2016}, abstract = {We present a novel hypothetical account of entrainment in music and language, in context of the Information Dynamics of Thinking model, IDyOT. The extended model affords an alternative view of entrainment, and its companion term, pulse, from earlier accounts. The model is based on hierarchical, statistical prediction, modeling expectations of both what an event will be and when it will happen. As such, it constitutes a kind of predictive coding, with a particular novel hypothetical implementation. Here, we focus on the model{\textquoteright}s mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic. We propose a range of tests to validate or falsify the model, at various different levels of abstraction, and argue that computational modeling in general, and this model in particular, can offer a means of providing limited but useful evidence for evolutionary hypotheses.}, keywords = {anticipatory cognition, cognitive architecture, evolutionary psychology, language, music perception, timing}, author = {Jamie Forth and Kat Agres and Matt Purver and Geraint Wiggins} } @article {39, title = {From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation}, journal = {Journal of Artificial General Intelligence}, volume = {6}, year = {2015}, pages = {55-86}, abstract = {We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.}, keywords = {behavioural validation, computational creativity, concept discovery, conceptual spaces, distributional semantics}, issn = {1946-0163}, doi = {10.1515/jagi-2015-0004}, url = {http://www.degruyter.com/view/j/jagi.2015.6.issue-1/jagi-2015-0004/jagi-2015-0004.xml?format=INT$\#$fn1}, author = {Stephen McGregor and Kat Agres and Matthew Purver and Geraint A. Wiggins} }