Dimos Makris

• PhD
• Singapore University of Technology and Design

This paper explores different approaches to harmony tokenization in symbolic music for transformer-based models, focusing on two tasks: masked language modeling (MLM) and melodic harmonization generation. Four tokenization strategies are compared, each varying in how chord information is encoded: (1) as full chord symbols, (2) separated into root a...

Guitar tablature transcription consists in deducing the string and the fret number on which each note should be played to reproduce the actual musical part. This assignment should lead to playable string-fret combinations throughout the entire track and, in general, preserve parsimonious motion between successive combinations. Throughout the histor...

The field of automatic music composition has seen great progress in recent years, specifically with the invention of transformer-based architectures. When using any deep learning model which considers music as a sequence of events with multiple complex dependencies, the selection of a proper data representation is crucial. In this paper, we tackle...

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...

The field of automatic music composition has seen great progress in recent years, specifically with the invention of transformer-based architectures. When using any deep learning model which considers music as a sequence of events with multiple complex dependencies, the selection of a proper data representation is crucial. In this paper, we tackle...

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),...

The rise of deep learning technologies has quickly advanced many fields, including generative music systems. There exists a number of systems that allow for the generation of musically sounding short snippets, yet, these generated snippets often lack an overarching, longer-term structure. In this work, we propose CM-HRNN: a conditional melody gener...

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),...

The rise of deep learning technologies has quickly advanced many fields, including that of generative music systems. There exist a number of systems that allow for the generation of good sounding short snippets, yet, these generated snippets often lack an overarching, longer-term structure. In this work, we propose CM-HRNN: a conditional melody gen...

Machine Learning has been shown a successful component of methods for Automatic Music Composition (AMC). Considering music as a sequence of events with multiple complex dependencies on various levels of a composition, the Long Short-Term Memory-based (LSTM) architectures have been proven to be very efficient in learning and reproducing musical styl...

Considering music as a sequence of events with multiple complex dependencies, the Long Short-Term Memory (LSTM) architecture has proven very efficient in learning and reproducing musical styles. However, the generation of rhythms requires additional information regarding musical structure and accompanying instruments. In this paper we present DeepD...

Algorithmic music composition has long been in the spotlight of music information research and Long Short-Term Memory (LSTM) neural networks have been extensively used for this task. However, despite LSTM networks having proven useful in learning sequences, no methodology has been proposed for learning sequences conditional to constraints, such as...

How can harmony in diverse idioms be represented in a machine learning system and how can learned harmonic descriptions of two musical idioms be blended to create new ones? This paper presents a creative melodic harmonisation assistant that employs statistical learning to learn harmonies from human annotated data in practically any style, blends th...

Human music listeners are capable of identifying multiple `voices' in musical content. This capability of grouping notes of polyphonic musical content into entities is of great importance for numerous processes of the Music Information Research domain, most notably for the better understanding of the underlying musical content's score. Accordingly,...

Melodic harmonisation is a sophisticated creative process that involves deep musical understanding and a specialised knowledge of music relating to melodic structure, harmony, rhythm, texture, and form. In this article a new melodic harmonisation assistant is presented that is adaptive (learns from data), general (can cope with any tonal or non-ton...

Automatic melodic harmonization tackles the assignment of harmony content (musical chords) over a given melody. Probabilistic approaches to melodic harmonization utilize statistical information derived from a training dataset, producing harmonies that encapsulate some harmonic characteristics of the training dataset. Training data is usually annota...

Music Information Research (MIR) requires musical data in order to test methods and to compare results. Greek music presents a number of unique characteristics that make its musical pieces distinct from popular tracks existing in currently available datasets, leading thus to the MIR requirement of Greek datasets. This work presents the Greek Music...

Melodic harmonisation deals with the assignment of harmony (chords) over a given melody. Probabilistic approaches to melodic harmonisation utilise statistical information derived from a training dataset to harmonise a melody. This paper proposes a probabilistic approach for the automatic generation of voice leading for the bass note on a set of giv...

Probabilistic methodologies provide successful tools for automated music composition, such as melodic harmoni-sation, since they capture statistical rules of the music idioms they are trained with. Proposed methodologies focus either on specific aspects of harmony (e.g., generating abstract chord symbols) or incorporate the determination of many ha...

The Greek Audio Dataset (GAD), is a freely available collection of audio features and metadata for a thousand popular Greek tracks. In this work, the creation process of the dataset is described together with its contents. Following the methodology of existing datasets, the GAD dataset does not include the audio content of the respective data due t...