@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}
}
@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 Müller, 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}
}
@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}
}
@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}
}
@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 = {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}
}
@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}
}
@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 = {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}
}
