Kat R Agres

Kat R Agres
National University of Singapore | NUS · Yong Siew Toh Conservatory of Music

PhD

About

52
Publications
12,696
Reads
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475
Citations
Introduction
Dr Kat Agres is an Assistant Professor at the Yong Siew Toh Conservatory of Music at the National University of Singapore (NUS), and has a joint appointment at Yale-NUS. She teaches on topics relating to Music Cognition and Music & Health, at the YST Conservatory, Yale-NUS, and the NUS Medical School. Kat's research focuses on the cognitive science of music perception and cognition, computational creativity, and music & healthcare.
Additional affiliations
January 2020 - present
National University of Singapore
Position
  • Professor (Assistant)
Description
  • I spearhead music cognition research; conduct research/outreach at the intersection of music, technology, and healthcare; and teach Music Cognition at the university.
July 2018 - December 2019
National University of Singapore
Position
  • Professor (Assistant)
Description
  • I teach Music Cognition at NUS, and support research activities at the Conservatory of Music.
January 2017 - present
Agency for Science, Technology and Research (A*STAR)
Position
  • Principal Investigator

Publications

Publications (52)
Article
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 type...
Article
Research into vision has highlighted the importance of gist representations in change detection and memory. This article 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 ex...
Article
Full-text available
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’s affective state such that a closed-loop interaction between the participant’s brain response...
Article
Full-text available
Statistical learning (SL) is the ability to generate predictions based on probabilistic dependencies in the environment, an ability that is present throughout life. The effect of aging on SL is still unclear. Here, we explore statistical learning in healthy adults (40 younger and 40 older). The novel paradigm tracks learning trajectories and shows...
Preprint
Full-text available
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 ‘Music, Computing, and Health’ was held to discuss best practices and state-of-the-art at the inters...
Preprint
Full-text available
Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory a...
Preprint
Full-text available
Stroke can have a severe impact on an individual's quality of life, leading to consequences such as motor loss and communication problems, especially among the elderly. Studies have shown that early and easy access to stroke rehabilitation can improve an elderly individual's quality of life, and that telerehabilitation is a solution that facilitate...
Article
Full-text available
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 p...
Conference Paper
Full-text available
The field of automatic music composition has seen great progress in the last few years, much of which can be attributed to advances in deep neural networks. There are numerous studies that present different strategies for generating sheet music from scratch. The inclusion of high-level musical characteristics (e.g., perceived emotional qualities),...
Preprint
Full-text available
The field of automatic music composition has seen great progress in the last few years, much of which can be attributed to advances in deep neural networks. There are numerous studies that present different strategies for generating sheet music from scratch. The inclusion of high-level musical characteristics (e.g., perceived emotional qualities),...
Article
Full-text available
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 “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the inters...
Preprint
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...
Article
Full-text available
In this paper, we present nnAudio, a new neural network-based audio processing framework with graphics processing unit (GPU) support that 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 t...
Article
Full-text available
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 metr...
Preprint
Full-text available
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...
Preprint
Full-text available
In this paper, we present \nbh{nnAudio}, a new neural network-based audio processing framework with graphics processing unit (GPU) support that 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 spectrogram...
Preprint
Full-text available
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 ide...
Preprint
Statistical learning (SL) is a profound mechanism of learning that is already present during infancy. However, SL in the elderly has received far less attention and its relation to general cognitive function remains elusive. Here, we explore statistical learning in 40 healthy elderly and 40 young adults. The paradigm deployed tracks learning trajec...
Preprint
Full-text available
Statistical Learning (SL), the ability to extract probabilistic information from the environment, is a subject of much debate. It appears intuitive that such a profound mechanism of learning should carry predictive power towards general cognitive ability. Yet, previous attempts have struggled to link SL ability to measures of general cognitive func...
Conference Paper
Full-text available
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 preven...
Preprint
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 preven...
Preprint
Full-text available
In this paper, we learn disentangled representations of timbre and pitch for musical instrument sounds. We adapt a framework based on variational autoencoders with Gaussian mixture latent distributions. Specifically, we use two separate encoders to learn distinct latent spaces for timbre and pitch, which form Gaussian mixture components representin...
Chapter
The act of listening to music to reach altered states of consciousness is common across many different cultures around the world, ranging from tribal settings in Central Java, Indonesia, to EDM (electronic dance music) dance clubs in the Western world. Despite the widespread listenership to trance music, we lack a comprehensive, scientific account...
Article
Full-text available
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 relationshi...
Preprint
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 metr...
Article
Full-text available
We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex poly-phonic) 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 met...
Conference Paper
Full-text available
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 imp...
Conference Paper
Full-text available
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 ther...
Conference Paper
Full-text available
We propose a system to automatically assess the intelligibility of sung lyrics. We are particularly interested in being able to identify songs which are intelligible to second language learners, as such individuals often sing along the song to help them learn their second language, but this is only helpful if the song is intelligible enough for the...
Conference Paper
Full-text available
Tonal structure is in part conveyed by statistical regularities between musical events, and research has shown that computational models reflect tonal structure in music by capturing these regularities in schematic constructs like pitch histograms. Of the few studies that model the acquisition of perceptual learning from musical data, most have emp...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
Full-text available
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...
Conference Paper
Full-text available
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 d...
Conference Paper
Full-text available
Whereas previous studies have examined the effect of rhythmic structures in trance music, the present research explores the impact of harmonic repetition on enjoyment through empirical testing. A number of uplifting trance (UT) excerpts were generated with different semiotic patterns (structures defining the order of chords in the sequence) each wi...
Conference Paper
Full-text available
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...
Article
Full-text available
We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discov- ering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynami...
Conference Paper
Full-text available
This paper puts forth a method for discovering computationally-derived conceptual spaces that reflect human conceptualization of musical and poetic creativity. We describe a lexical space that is defined through co-occurrence statistics, and compare the dimensions of this space with human responses on a word association task. Participants' response...
Conference Paper
Full-text available
A salient characteristic of human perception of music is that musical events are perceived as being grouped temporally into structural units such as phrases or motifs. Segmentation of musical sequences into structural units is a topic of ongoing research, both in cognitive psychology and music information retrieval. Computational models of music se...
Conference Paper
Full-text available
In this paper, we propose an auditory search task using a virtual ambisonic environment presented through static Head-Related Transfer Functions (HRTF’s). Head-tracking using a magnetometer captures the listener’s orientation and presents an interactive auditory scene. Reaction times from 15 participants are compared for Simple and Complex auditory...
Article
Full-text available
Evidence suggests that sparse coding allows for a more efficient and effective way to distill structural information about the environment. Our simple recurrent network has demonstrated the same to be true of learning musical structure. Two experiments are presented that examine the learning trajectory of a simple recurrent network exposed to music...
Article
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's theoretical framework on musical expec...
Conference Paper
Full-text available
This article presents two experiments investigating the degree to which listeners can detect changes in melodies. In both studies, pairs of melodies were presented to a group of professional musicians and a group of non-musicians. In Experiment 1, musical structure and musical expertise were explored with stylistic, non-stylistic, and random melodi...

Questions

Question (1)
Question
In your opinion, what are the most innovative, ground-breaking, or creative (and effective) music-based technologies and interventions to promote health or well-being? I'm compiling a list! Academic research publications and industry research/technologies welcome. :)

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Projects

Projects (4)
Project
We are developing music-based software and technology to support applications in healthcare and well-being. Given the ageing population, and the high incidence of stroke, one focus has been to develop motion-detection and music technology to support cognitive and motor rehabilitation in stroke patients, as well as strengthening and mental health in the elderly.