@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 = {AffectMachine-Classical: a novel system for generating affective classical music}, journal = {Frontiers in Psychology 14:1158172. doi: 10.3389/fpsyg.2023.1158172}, volume = {14:1158172}, year = {2023}, month = {06/2023}, pages = {1-13}, type = {Journal article}, abstract = {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.}, keywords = {affective computing, algorithmic composition, automatic music generation system, emotion regulation, listener validation study, Music MedTech}, doi = {doi.org/10.3389/fpsyg.2023.1158172}, url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1158172/full}, author = {Kat R. Agres and Adyasha Dash and Phoebe Chua} } @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} } @article {2023, title = {MERP: A Music Dataset with Emotion Ratings and Raters{\textquoteright} Profile Information}, journal = {Sensors}, volume = {23}, year = {2023}, month = {12/2022}, pages = {1-23}, abstract = {Abstract: 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.}, keywords = {emotion prediction; music; music emotion dataset; affective computing}, doi = {https://doi.org/10.3390/s23010382}, url = {https://www.mdpi.com/1424-8220/23/1/382}, author = {En Yan Koh and Kin Wai Cheuk and Kwan Yee Heung and Kat R. Agres and Dorien Herremans} } @proceedings {2022, title = {Comparing Musicians and Non-musicians{\textquoteright} Expectations in Music and Vision}, year = {2022}, month = {09/2022}, pages = {1-8}, publisher = {ACM}, address = {Austria and online}, abstract = {The role of expectation in music has been of research interest for decades. Expectation mechanisms have also received considerable attention in vision, due in part to the widespread interest in predictive coding. Past research has uncovered different types of expectations that may be formed when exposed to a sequential stimulus, such as schematic expectation (based on general knowledge) and dynamic expectation (based on properties within the current stimulus). Yet to our knowledge, a direct comparison of the relative contribution of these types of expectation has not been performed within the same subjects through careful manipulation of stimuli, nor has this comparison been made across the musical and visual domains. This listener study aims to uncover the relative influence of dynamic and schematic expectations in musical and visual stimuli, and investigate the role of expertise in forming expectations by testin g both musicians and non-musicians. Our findings suggest that musicians are indeed more sensitive than non-musicians to the dynamic and schematic properties of musical stimuli, and they generally produce a wider range of expectedness ratings than non-musicians. Interestingly, musicians also interpret schematic information in the visual condition differently than non-musicians, suggesting that musical training may have influenced their expectation mechanisms more generally.}, keywords = {cross-modal comparison, Expectation, music, musician vs non-musician, vision}, author = {Kat R Agres and Ting Yuan Tay and Marcus Pearce} } @conference {2022, title = {Computational Music Systems for Emotional Health and Wellbeing: A Review}, booktitle = {ACM Special Interest Group on Computer{\textendash}Human Interaction (SIGHCI) 2022}, year = {2022}, month = {12/2022}, abstract = {Music is a powerful stimulus, and both active and receptive methods of engaging with music provide affordances for improving physical, mental and social health. The emergence of sophisticated computational methods also underscores the potential for novel music technologies to address a wider range of wellbeing outcomes. In this review, we focus on describing the current state of the literature on computational approaches to music generation for health and wellbeing and identifying possible future directions for research in this area.}, author = {Phoebe Chua and Chitralekha Gupta and Kat R. Agres and Suranga Nanayakkara} } @proceedings {2022, title = {Engaging deeply with cognitive science in a conservatory of music: Using student-centered learning to enhance learning outcomes in music students}, year = {2022}, month = {07/2022}, pages = {1-15}, abstract = {Concepts from cognitive science (CS), including music psychology and performance science, are rarely taught to conservatory students, even though these topics are likely useful for their practice and performance of music. This study examines the effectiveness of student-centered learning (SCL) and project-based learning (PBL) methods for teaching CS to undergraduate conservatory students in a module on The Psychology of Music Performance. The results gleaned from student assignments and feedback show that the module activities, especially the SCL and PBL components, were largely successful in deeply engaging the students, resulting in knowledge acquisition as well as critical thinking. The findings suggest that a well-balanced teaching approach incorporating elements of SCL and PBL is capable of instilling both breadth and depth of learning of relevant CS findings in the music conservatory context.}, keywords = {cognitive science, ISME conference, music education}, author = {Kat R Agres and Natasha Ureyang} } @proceedings {2022, title = {Improving Students{\textquoteright} Practice and Performance of Music by Incorporating Cognitive Science into Conservatory Curricula}, year = {2022}, month = {07/2022}, pages = {1-13}, abstract = {Relatively few music educators integrate relevant research findings from the field of cognitive science into their classroom teaching, despite the many benefits that engaging with this area can have for the practice and performance of music. In this paper, we examine a new module that aimed to teach cognitive and performance science to conservatory students. The findings from our study suggest that the module helped students practice more efficiently, tackle challenges during practice sessions, approach practice more analytically, discover mind-body connections, and perform more confidently. Further, the module fostered a sense of community among the students. The successes of this module provide a strong case for the benefits of incorporating cognitive science into the curriculum of higher music education.}, keywords = {cognitive science, ISME conference, music education}, author = {Kat R Agres and Natasha Ureyang} } @article {2021, title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for health care and well-being}, journal = {Music \& Science}, year = {2021}, abstract = {The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop {\textquoteleft}Music, Computing, and Health{\textquoteright} was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech) and robotics. Following the discussions at the workshop, this paper provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the paper summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that 1) supports person-centered care and evidence-based treatments, and 2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The paper provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.}, keywords = {Computing, Health, Music Information Retrieval, Music Psychology, Music Therapy, Roadmap, Technology}, doi = {https://doi.org/10.1177/2059204321997709}, author = {Agres, Kat R and Schaefer, Rebecca and Volk, Anja and van Hooren, Susan and Holzapfel, Andre and DallaBella, Simone and Mùˆller, Meinard and de Witte, Martina and Herremans, Dorien and Ramirez-Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy} } @article {2021, title = {Music Therapy During COVID-19: Changes to the Practice, Use of Technology, and What to Carry Forward in the Future}, journal = {Frontiers in Psychology}, volume = {12}, year = {2021}, month = {05/2021}, chapter = {1317}, abstract = {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{\textquoteright} 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{\textquoteright} 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.}, keywords = {COVID-19 pandemic, music technology, Music Therapy, online tools, regional comparisons, regional practices, teletherapy}, doi = {DOI=10.3389/fpsyg.2021.647790}, url = {https://www.frontiersin.org/article/10.3389/fpsyg.2021.647790}, author = {Agres, Kat R. and Foubert, Katrien and Sridhar, Siddarth} } @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 {2019, title = {Age dependent statistical learning trajectories reveal differences in information weighting}, journal = {Psychology and Aging}, year = {2020}, abstract = {Statistical learning (SL) is the ability to generate predictions based on probabilistic dependencies in the environment, an ability that is present throughout life. However, SL in older adults has received far less attention. 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 three different learning strategies: (1) Win-Stay, Lose-Shift, (2) Delta Rule Learning, (3) 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 behaviour, but also show disproportionally strong reactions towards erroneous predictions. Taken together, the present pattern of results points towards age-related differences in information processing, with lower but more balanced information weights in older adults. }, keywords = {Cognitive Assessment, Continuous Paradigm, Elderly, Statistical Learning}, doi = {10.31234/osf.io/kuy6p}, url = {psyarxiv.com/kuy6p}, author = {Herff, Steffen A and Zhen, Shanshan and Yu, Rongjun and Agres, Kat R} } @conference {9998, title = {A dataset and classification model for Malay, Hindi, Tamil and Chinese music}, booktitle = {Workshop on Machine Learning and Music (MML 2020), at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) conference}, year = {2020}, abstract = {In this paper we present a new dataset, with musical excepts from the three main ethnic groups in Singapore: Chinese, Malay and Indian (both Hindi and Tamil). We use this new dataset to train different classification models to distinguish the origin of the music in terms of these ethnic groups. The classification models were optimized by exploring the use of different musical features as the input. Both high level features, i.e., musically meaningful features, as well as low level features, i.e., spectrogram based features, were extracted from the audio files so as to optimize the performance of the different classification models. }, url = {https://arxiv.org/abs/2009.04459}, author = {F Nahar and K Agres and B Balamurali and D Herremans} } @proceedings {2020, title = {Developing Music Technology for Emotion Regulation and Motor Rehabilitation}, year = {2020}, abstract = {Medical technology (MedTech) involving music is increasingly being developed for various clinical and non-clinical contexts to support health and well-being. This paper focuses on two recent systems that allow users to steer their own wellness and recovery: a BCI system for emotion self-regulation in listeners (Ehrlich, Agres, Guan, \& Cheng, 2019), and a motion-capture game for motor rehab (Agres \& Herremans, 2017).}, keywords = {brain-computer interfaces, emotion regulation, Motor Rehabilitation, music \& medicine, Music MedTech, music technology, serious games}, author = {Kat R. Agres} } @conference {2020, title = {The impact of Audio input representations on neural network based music transcription}, booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, year = {2020}, month = {07/2020}, address = {Glasgow}, abstract = {This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription. We use our own GPU based spectrogram extraction tool, nnAudio, to investigate the influence of using a linear-frequency spectrogram, log-frequency spectrogram, Mel spectrogram, and constant-Q transform (CQT). Our results show that a 8.33\% increase in transcription accuracy and a 9.39\% reduction in error can be obtained by choosing the appropriate input representation (log-frequency spectrogram with STFT window length 4,096 and 2,048 frequency bins in the spectrogram) without changing the neural network design (single layer fully connected). Our experiments also show that Mel spectrogram is a compact representation for which we can reduce the number of frequency bins to only 512 while still keeping a relatively high music transcription accuracy.}, url = {https://arxiv.org/abs/2001.09989}, author = {K.W. Cheuk and K. 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} } @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 = {nnAudio: A PyTorch Audio Processing Tool Using 1D Convolution neural networks}, booktitle = {ISMIR - Late Breaking Demo}, year = {2019}, month = {10/2019}, address = {Delft, The Netherlands}, author = {K.W. Cheuk 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 = {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} } @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} } @article {9998, title = {From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec}, journal = {Neural Computing and Applications}, volume = {https://doi.org/10.1007/s00521-018-3923-1}, year = {2018}, abstract = {We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex polyphonic) music. More precisely, the skip-gram version of word2vec is used to model slices of music from a large corpus spanning eight musical genres. In this newly learned vector space, a metric based on cosine distance is able to distinguish between functional chord relationships, as well as harmonic associations in the music. Evidence, based on cosine distance between chord-pair vectors, suggests that an implicit circle-of-fifths exists in the vector space. In addition, a comparison between pieces in different keys reveals that key relationships are represented in word2vec space. These results suggest that the newly learned embedded vector representation does in fact capture tonal and harmonic characteristics of music, without receiving explicit information about the musical content of the constituent slices. In order to investigate whether proximity in the discovered space of embeddings is indicative of {\textquoteleft}semantically-related{\textquoteright} slices, we explore a music generation task, by automatically replacing existing slices from a given piece of music with new slices. We propose an algorithm to find substitute slices based on spatial proximity and the pitch class distribution inferred in the chosen subspace. The results indicate that the size of the subspace used has a significant effect on whether slices belonging to the same key are selected. In sum, the proposed word2vec model is able to learn music-vector embeddings that capture meaningful tonal and harmonic relationships in music, thereby providing a useful tool for exploring musical properties and comparisons across pieces, as a potential input representation for deep learning models, and as a music generation device.}, keywords = { semantic vector space model, music, word2vec}, author = {Chuan, C-H and Agres, K and Herremans, D} } @article {2017, title = {Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory}, journal = {Cognitive Science}, volume = {42}, year = {2018}, month = {01/2018}, pages = {43-76}, abstract = {A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners{\textquoteright} memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners{\textquoteright} expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session.}, keywords = {Auditory perception, computational modeling, Expectation, Information theory, music cognition, Predictive coding, Recognition memory}, issn = {1551-6709}, doi = {10.1111/cogs.12477}, url = {http://dx.doi.org/10.1111/cogs.12477}, author = {Agres, Kat and Abdallah, Samer and Pearce, Marcus} } @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} } @proceedings {2018, title = {Rhythmic Entrainment for Hand Rehabilitation Using the Leap Motion Controller}, year = {2018}, month = {09/2018}, address = {Paris, France.}, abstract = {Millions of individuals around the world suffer from motor impairment or disability, yet effective, engaging, and cost-effective therapeutic solutions are still lacking. In this work, we propose a game for hand rehabilitation that leverages the therapeutic aspects of music for motor rehabilitation, incorporates the power of gamification to improve adherence to medical treatment, and uses the versatility of devices such as the Leap Motion Controller to track users{\textquoteright} movements. The main characteristics of the game as well as future research directions are outlined.}, keywords = {Leap Motion, Motor Rehabilitation, music, Music Therapy, Rhythmic Entrainment}, author = {Beveridge, S. and Cano, E. and Agres, K.} } @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} } @article {2017, title = {Harmonic Structure Predicts the Enjoyment of Uplifting Trance Music}, journal = {Frontiers in Psychology}, volume = {7}, year = {2017}, pages = {1999}, abstract = {An empirical investigation of how local harmonic structures (e.g., chord progressions) contribute to the experience and enjoyment of uplifting trance (UT) music is presented. The connection between rhythmic and percussive elements and resulting trance-like states has been highlighted by musicologists, but no research, to our knowledge, has explored whether repeated harmonic elements influence affective responses in listeners of trance music. Two alternative hypotheses are discussed, the first highlighting the direct relationship between repetition/complexity and enjoyment, and the second based on the theoretical inverted-U relationship described by the Wundt curve. We investigate the connection between harmonic structure and subjective enjoyment through interdisciplinary behavioral and computational methods: First we discuss an experiment in which listeners provided enjoyment ratings for computer-generated UT anthems with varying levels of harmonic repetition and complexity. The anthems were generated using a statistical model trained on a corpus of 100 uplifting trance anthems created for this purpose, and harmonic structure was constrained by imposing particular repetition structures (semiotic patterns defining the order of chords in the sequence) on a professional UT music production template. Second, the relationship between harmonic structure and enjoyment is further explored using two computational approaches, one based on average Information Content, and another that measures average tonal tension between chords. The results of the listening experiment indicate that harmonic repetition does in fact contribute to the enjoyment of uplifting trance music. More compelling evidence was found for the second hypothesis discussed above, however some maximally repetitive structures were also preferred. Both computational models provide evidence for a Wundt-type relationship between complexity and enjoyment. By systematically manipulating the structure of chord progressions, we have discovered specific harmonic contexts in which repetitive or complex structure contribute to the enjoyment of uplifting trance music.}, issn = {1664-1078}, doi = {10.3389/fpsyg.2016.01999}, url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2016.01999}, author = {Agres, Kat and Herremans, Dorien and Bigo, Louis and Conklin, Darrell} } @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} } @proceedings {2017, title = {Music and Motion-Detection: A Game Prototype for Rehabilitation and Strengthening in the Elderly}, year = {2017}, month = {12/2017}, publisher = {IEEE}, abstract = {Traditional physical therapy methods require significant time from trained medical staff, which is costly for clinics and hospitals, and often leave patients bored and unmotivated to complete their exercises. We offer a prototype for a motion- detection and music game to inspire greater engagement and adherence from patients undergoing physical therapy exercises for rehabilitation or strengthening. The game is customizable based on the patient{\textquoteright}s needs, dynamically reacts to the patient{\textquoteright}s performance in real-time, and may be used with or without the guidance of a medical professional.}, keywords = {gaming, motion detection, motion-capture, music, physical therapy, preventive medicine, rehabilitation}, author = {Agres, Kat and Herremans, Dorien} } @proceedings {2016, title = {The Effect of Repetitive Structure on Enjoyment in Uplifting Trance Music}, year = {2016}, month = {07/2016}, pages = {280-282}, address = {San Francisco}, isbn = {1-876346-65-5}, author = {K. Agres and L. Bigo and D. Herremans and D. Conklin} } @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 {2017, title = {Evaluation of Musical Creativity and Musical Metacreation Systems}, journal = {ACM Comput. Entertain.}, volume = {14}, year = {2016}, month = {12/2016}, pages = {3:1{\textendash}3:33}, abstract = {The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher and not objectively at all. This article provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers (1) evaluate their systems{\textquoteright} creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and (2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system and may be employed by the researcher to both better understand the efficacy of their system and its impact and to incorporate feedback into the system. Here we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition and can be used to demonstrate the impact and novelty of particular approaches.}, keywords = {artificial intelligence, Music perception and cognition}, issn = {1544-3574}, doi = {10.1145/2967506}, url = {http://doi.acm.org/10.1145/2967506}, author = {Agres, Kat and Forth, Jamie and Wiggins, Geraint A.} } @proceedings {2016, title = {The Information Dynamics of Thinking: a cognitive architecture for human creative cognition}, year = {2016}, month = {2016}, abstract = {We present IDyOT (Wiggins, 2012b), the Information Dynamics of Thinking, a novel cognitive architecture that instantiates the (much more abstract) Global Workspace Theory of Baars (1988), in a way motivated by a principle of efficiency in information processing. IDyOT simulates a hypothetical fundamental perceptual/cognitive loop, explicating bottom-up learning of concepts, the hierarchical structure in them, and the correlations between them from percepts. Even though the proposal is basically cognitive, embodiment is not ruled out, and the tight connection between perception and cognition potentially affords a future extension of the account to an embodied view.}, author = {Wiggins, Geraint A and Agres, Kat and Forth, Jamie and Purver, Matt} } @conference {2016, title = {Meta4meaning: Automatic Metaphor Interpretation Using Corpus-Derived Word Associations}, booktitle = {Proceedings of the Seventh International Conference on Computational Creativity}, year = {2016}, month = {June}, address = {Paris, France}, abstract = {We propose a novel metaphor interpretation method, Meta4meaning. It provides interpretations for nominal metaphors by generating a list of properties that the metaphor expresses. Meta4meaning uses word associations extracted from a corpus to retrieve an approximation to properties of concepts. Interpretations are then obtained as an aggregation or difference of the saliences of the properties to the tenor and the vehicle. We evaluate Meta4meaning using a set of \mgwed{human-annotated} interpretations of 84 metaphors and compare with two existing methods for metaphor interpretation. Meta4meaning significantly outperforms the previous methods on this task.}, author = {Xiao, Ping and Alnajjar, Khalid and Granroth-Wilding, Mark and Agres, Kathleen and Toivonen, Hannu} } @proceedings {2016, title = {Modeling metaphor perception with distributional semantics vector space models}, year = {2016}, month = {08/2016}, abstract = {In this paper, we present a novel application of a computational model of word meaning to capture human judgments of the linguistic properties of metaphoricity, familiarity, and meaningfulness. We present data gathered from human subjects regarding their ratings of these properties over a set of word pairs specifically designed to exhibit varying degrees of metaphoricity. We then investigate whether these properties can be measured in terms of geometric features of a model of distributional lexical semantics. We compare the performance of two models, our own Concept Discovery Model which dynamically constructs context-sensitive subspaces, and a state-of-the-art static distributional semantic model, and find that our dynamic model performs significantly better in its measurement of metaphoricity.}, keywords = {computational creativity, distributional semantics, metaphor, vector space models}, author = {Kat R. Agres and Stephen McGregor and Karolina Rataj and Matthew Purver and Geraint A. Wiggins} } @article {2016, title = {Workshop on Auditory Neuroscience, Cognition, and Modeling}, journal = {Psychomusicology: Music, Mind, and Brain}, volume = {26}, year = {2016}, month = {09/2016}, pages = {288-292}, abstract = {Early 2016 saw the first international Workshop on Auditory Neuroscience, Cognition and Modeling, hosted by Queen Mary University of London. The workshop aimed to bring together cognitive scientists, neuroscientists, and computer scientists working on sound, music, and speech processing. Engendering broad interest, 96 participants from a wide range of backgrounds attended for a day of talks, poster presentations, and lively discussion. This report provides an overview of the event, summarizing the 3 keynote talks, and highlighting the major themes from the oral presentation and poster sessions. A summary is provided in closing, touching upon main outcomes and considerations for future research within this alignment of fields.}, keywords = {auditory neuroscience, computational modeling, music cognition, speech perception, WANCM}, author = {Agres, Kat R and Sauv{\'e}, Sarah A} } @conference {27, title = {Conceptualizing Creativity: From Distributional Semantics to Conceptual Spaces}, booktitle = {Proceedings of the Sixth International Conference on Computational Creativity}, year = {2015}, author = {Agres, Kat and McGregor, Stephen and Purver, Matthew and Wiggins, Geraint} } @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} } @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} } @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} } @proceedings {34, title = {Schematic processing as a framework for learning and creativity in CBR and CC}, year = {2015}, pages = {151-155}, publisher = {ICCBR-15}, author = {Agres, Kat and Wiggins, Geraint A.} } @conference {29, title = {An information-theoretic account of musical expectation and memory}, booktitle = {Proceedings of the 35th Annual Conference of the Cognitive Science Society}, year = {2013}, author = {Agres, Kat and Abdallah, Samer and Pearce, Marcus} } @mastersthesis {31, title = {The Learning Trajectory Of Musical Memory: From Schematic Processing Of Novel Melodies To Robust Musical Memory Representations}, volume = {PhD}, year = {2013}, school = {Cornell University}, type = {PhD}, author = {Agres, Kathleen} } @conference {32, title = {An auditory twist on visual search using head movement controlled ambisonics}, booktitle = {International Computer Music Conference}, year = {2010}, publisher = {Ann Arbor, MI: MPublishing, University of Michigan Library}, organization = {Ann Arbor, MI: MPublishing, University of Michigan Library}, author = {Agres, Kat and Spencer Topel and Michael Spivey and Stephen Moseson} } @conference {26, title = {The sparsity of simple recurrent networks in musical structure learning}, booktitle = {Proceedings of the 31th Annual Conference of the Cognitive Science Society}, year = {2009}, publisher = {Citeseer}, organization = {Citeseer}, author = {Agres, Kat R and DeLong, Jordan E and Spivey, Michael} } @conference {25, title = {Musical change deafness: The inability to detect change in a non-speech auditory domain}, booktitle = {In B. C. Love, K. McRae, \& V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 969-974)}, year = {2008}, author = {Agres, Kat R and Krumhansl, Carol L} } @article {28, title = {Musical expectancy: The influence of musical structure on emotional response}, journal = {Behavioral and Brain Sciences}, volume = {31}, year = {2008}, month = {10/2008}, pages = {584{\textendash}585}, abstract = {When examining how emotions are evoked through music, the role of musical expectancy is often surprisingly under-credited. This mechanism, however, is most strongly tied to the actual structure of the music, and thus is important when considering how music elicits emotions. We briefly summarize Leonard Meyer{\textquoteright}s theoretical framework on musical expectancy and emotion and cite relevant research in the area.}, doi = {https://doi.org/10.1017/S0140525X08005384}, author = {Krumhansl, Carol L and Agres, Kat R} }