@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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.}
}
@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 {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}
}
@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}
}
