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139 results on '"Kaliakatsos-Papakostas, Maximos"'

2. A Dataset for Greek Traditional and Folk Music: Lyra

Author

Papaioannou, Charilaos, Valiantzas, Ioannis, Giannakopoulos, Theodoros, Kaliakatsos-Papakostas, Maximos, and Potamianos, Alexandros

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Studying under-represented music traditions under the MIR scope is crucial, not only for developing novel analysis tools, but also for unveiling musical functions that might prove useful in studying world musics. This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre, among others. The content has been collected from a Greek documentary series that is available online, where academics present music traditions of Greece with live music and dance performance during the show, along with discussions about social, cultural and musicological aspects of the presented music. Therefore, this procedure has resulted in a significant wealth of descriptions regarding a variety of aspects, such as musical genre, places of origin and musical instruments. In addition, the audio recordings were performed under strict production-level specifications, in terms of recording equipment, leading to very clean and homogeneous audio content. In this work, apart from presenting the dataset in detail, we propose a baseline deep-learning classification approach to recognize the involved musicological attributes. The dataset, the baseline classification methods and the models are provided in public repositories. Future directions for further refining the dataset are also discussed.

Published
2022

3. Conditional Drums Generation using Compound Word Representations

Author

Makris, Dimos, Zixun, Guo, Kaliakatsos-Papakostas, Maximos, and Herremans, Dorien

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

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 task of conditional drums generation using a novel data encoding scheme inspired by the Compound Word representation, a tokenization process of sequential data. Therefore, we present a sequence-to-sequence architecture where a Bidirectional Long short-term memory (BiLSTM) Encoder receives information about the conditioning parameters (i.e., accompanying tracks and musical attributes), while a Transformer-based Decoder with relative global attention produces the generated drum sequences. We conducted experiments to thoroughly compare the effectiveness of our method to several baselines. Quantitative evaluation shows that our model is able to generate drums sequences that have similar statistical distributions and characteristics to the training corpus. These features include syncopation, compression ratio, and symmetry among others. We also verified, through a listening test, that generated drum sequences sound pleasant, natural and coherent while they "groove" with the given accompaniment., Comment: Accepted for the 11th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART), 2022

Published
2022

4. NLP-Theatre: Employing Speech Recognition Technologies for Improving Accessibility and Augmenting the Theatrical Experience

Author

Katsalis, Alkiviadis, Christantonis, Konstantinos, Tsioustas, Charalampos, Kaplanoglou, Pantelis I., Kaliakatsos-Papakostas, Maximos, Katsamanis, Athanasios, Diamantaras, Konstantinos, Katsouros, Vassilis, Fotinea, Evita, Panga, Depy, Loupi, Dimitra, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2023
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5. DeepDrum: An Adaptive Conditional Neural Network

Author

Makris, Dimos, Kaliakatsos-Papakostas, Maximos, and Kermanidis, Katia Lida

Subjects
Computer Science - Sound, Computer Science - Information Retrieval, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

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 DeepDrum, an adaptive Neural Network capable of generating drum rhythms under constraints imposed by Feed-Forward (Conditional) Layers which contain musical parameters along with given instrumentation information (e.g. bass and guitar notes). Results on generated drum sequences are presented indicating that DeepDrum is effective in producing rhythms that resemble the learned style, while at the same time conforming to given constraints that were unknown during the training process., Comment: 2018 Joint Workshop on Machine Learning for Music. The Federated Artificial Intelligence Meeting (FAIM), a joint workshop program of ICML, IJCAI/ECAI, and AAMAS

Published
2018

6. NLP-Theatre: Employing Speech Recognition Technologies for Improving Accessibility and Augmenting the Theatrical Experience

Author

Katsalis, Alkiviadis, primary, Christantonis, Konstantinos, additional, Tsioustas, Charalampos, additional, Kaplanoglou, Pantelis I., additional, Kaliakatsos-Papakostas, Maximos, additional, Katsamanis, Athanasios, additional, Diamantaras, Konstantinos, additional, Katsouros, Vassilis, additional, Fotinea, Evita, additional, Panga, Depy, additional, and Loupi, Dimitra, additional

Published
2022
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9. An Argument-based Creative Assistant for Harmonic Blending

Author

Kaliakatsos-Papakostas, Maximos, Confalonieri, Roberto, Corneli, Joseph, Zacharakis, Asterios, and Cambouropoulos, Emilios

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, H.5.5
Abstract

Conceptual blending is a powerful tool for computational creativity where, for example, the properties of two harmonic spaces may be combined in a consistent manner to produce a novel harmonic space. However, deciding about the importance of property features in the input spaces and evaluating the results of conceptual blending is a nontrivial task. In the specific case of musical harmony, defining the salient features of chord transitions and evaluating invented harmonic spaces requires deep musicological background knowledge. In this paper, we propose a creative tool that helps musicologists to evaluate and to enhance harmonic innovation. This tool allows a music expert to specify arguments over given transition properties. These arguments are then considered by the system when defining combinations of features in an idiom-blending process. A music expert can assess whether the new harmonic idiom makes musicological sense and re-adjust the arguments (selection of features) to explore alternative blends that can potentially produce better harmonic spaces. We conclude with a discussion of future work that would further automate the harmonisation process., Comment: 8 pp; submitted to 7th International Conference on Computational Creativity

Published
2016

11. Chapter 9 Conceptual Blending in Melodic Harmonisation: Development and Empirical Evaluation in the Case of the CHAMELEON System

Author

Kaliakatsos-Papakostas, Maximos, Zacharakis, Asterios, Cambouropoulos, Emilios, Pachet, François, Series Editor, Gervás, Pablo, Series Editor, Passerini, Andrea, Series Editor, Degli Esposti, Mirko, Series Editor, Confalonieri, Roberto, editor, Pease, Alison, editor, Schorlemmer, Marco, editor, Besold, Tarek R., editor, Kutz, Oliver, editor, Maclean, Ewen, editor, and Kaliakatsos-Papakostas, Maximos, editor

Published
2018
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12. Research in the Department of Music Technology and Acoustics of the Hellenic Mediterranean University: An Overview and Prospects

Author

Brezas, Spyros, primary, Paschalidou, Stella, additional, Alexandraki, Chrisoula, additional, Bakarezos, Makis, additional, Georgatou, Christine, additional, Kaleris, Konstantinos, additional, Kaliakatsos-Papakostas, Maximos, additional, Kaniolakis-Kaloudis, Emmanouil, additional, Kaselouris, Evaggelos, additional, Orphanos, Yannis, additional, Papadaki, Helen, additional, Papadogiannis, Nektarios A., additional, Tzedaki, Katerina, additional, Valsamakis, Nikolas, additional, and Dimitriou, Vasilis, additional

Published
2024
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13. Generating Drum Rhythms Through Data-Driven Conceptual Blending of Features and Genetic Algorithms

Author

Kaliakatsos-Papakostas, Maximos, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Liapis, Antonios, editor, Romero Cardalda, Juan Jesús, editor, and Ekárt, Anikó, editor

Published
2018
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15. Audio Recognition of the Percussion Sounds Generated by a 3D Auto-Drum Machine System via Machine Learning.

Author

Brezas, Spyros, Skoulakis, Alexandros, Kaliakatsos-Papakostas, Maximos, Sarantis-Karamesinis, Antonis, Orphanos, Yannis, Tatarakis, Michael, Papadogiannis, Nektarios A., Bakarezos, Makis, Kaselouris, Evaggelos, and Dimitriou, Vasilis

Subjects
MACHINE learning, DEEP learning, MICROPHONES, PERCUSSION instruments, SOUND recordings, SOUNDS
Abstract

A novel 3D auto-drum machine system for the generation and recording of percussion sounds is developed and presented. The capabilities of the machine, along with a calibration, sound production, and collection protocol are demonstrated. The sounds are generated by a drumstick at pre-defined positions and by known impact forces from the programmable 3D auto-drum machine. The generated percussion sounds are accompanied by the spatial excitation coordinates and the correspondent impact forces, allowing for large databases to be built, which are required by machine learning models. The recordings of the radiated sound by a microphone are analyzed using a pre-trained deep learning model, evaluating the consistency of the physical sample generation method. The results demonstrate the ability to perform regression and classification tasks when fine tuning the deep learning model with the gathered data. The produced databases can properly train machine learning models, aiding in the investigation of alternative and cost-effective materials and geometries with relevant sound characteristics and in the development of accurate vibroacoustic numerical models for studying percussion instruments sound synthesis. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Combining LSTM and Feed Forward Neural Networks for Conditional Rhythm Composition

Author

Makris, Dimos, Kaliakatsos-Papakostas, Maximos, Karydis, Ioannis, Kermanidis, Katia Lida, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Boracchi, Giacomo, editor, Iliadis, Lazaros, editor, Jayne, Chrisina, editor, and Likas, Aristidis, editor

Published
2017
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21. Modelling Cadence Perception Via Musical Parameter Tuning to Perceptual Data

Author

Kaliakatsos-Papakostas, Maximos, Zacharakis, Asterios, Tsougras, Costas, Cambouropoulos, Emilios, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Gulliksen, Jan, Series editor, Rauterberg, Matthias, Series editor, Iliadis, Lazaros, editor, and Maglogiannis, Ilias, editor

Published
2016
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22. Learning and Blending Harmonies in the Context of a Melodic Harmonisation Assistant

Author

Kaliakatsos-Papakostas, Maximos, Makris, Dimos, Zacharakis, Asterios, Tsougras, Costas, Cambouropoulos, Emilios, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Gulliksen, Jan, Series editor, Rauterberg, Matthias, Series editor, Iliadis, Lazaros, editor, and Maglogiannis, Ilias, editor

Published
2016
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27. evoDrummer: Deriving Rhythmic Patterns through Interactive Genetic Algorithms

Author

Kaliakatsos–Papakostas, Maximos A., Floros, Andreas, Vrahatis, Michael N., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Machado, Penousal, editor, McDermott, James, editor, and Carballal, Adrian, editor

Published
2013
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28. Interactive Evolution of 8–Bit Melodies with Genetic Programming towards Finding Aesthetic Measures for Sound

Author

Kaliakatsos–Papakostas, Maximos A., Epitropakis, Michael G., Floros, Andreas, Vrahatis, Michael N., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Machado, Penousal, editor, Romero, Juan, editor, and Carballal, Adrian, editor

Published
2012
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29. Feature Extraction Using Pitch Class Profile Information Entropy

Author

Kaliakatsos-Papakostas, Maximos A., Epitropakis, Michael G., Vrahatis, Michael N., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Agon, Carlos, editor, Andreatta, Moreno, editor, Assayag, Gérard, editor, Amiot, Emmanuel, editor, Bresson, Jean, editor, and Mandereau, John, editor

Published
2011
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30. Weighted Markov Chain Model for Musical Composer Identification

Author

Kaliakatsos-Papakostas, Maximos A., Epitropakis, Michael G., Vrahatis, Michael N., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Di Chio, Cecilia, editor, Brabazon, Anthony, editor, Di Caro, Gianni A., editor, Drechsler, Rolf, editor, Farooq, Muddassar, editor, Grahl, Jörn, editor, Greenfield, Gary, editor, Prins, Christian, editor, Romero, Juan, editor, Squillero, Giovanni, editor, Tarantino, Ernesto, editor, Tettamanzi, Andrea G. B., editor, Urquhart, Neil, editor, and Uyar, A. Şima, editor

Published
2011
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31. Musical Composer Identification through Probabilistic and Feedforward Neural Networks

Author

Kaliakatsos-Papakostas, Maximos A., Epitropakis, Michael G., Vrahatis, Michael N., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Di Chio, Cecilia, editor, Brabazon, Anthony, editor, Di Caro, Gianni A., editor, Ebner, Marc, editor, Farooq, Muddassar, editor, Fink, Andreas, editor, Grahl, Jörn, editor, Greenfield, Gary, editor, Machado, Penousal, editor, O’Neill, Michael, editor, Tarantino, Ernesto, editor, and Urquhart, Neil, editor

Published
2010
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33. NLP-Theatre: Employing Speech Recognition Technologies for Improving Accessibility and Augmenting the Theatrical Experience

Author

Katsalis, Alkiviadis, Christantonis, Konstantinos, Tsioustas, Charalampos, Kaplanoglou, Pantelis I., Kaliakatsos-Papakostas, Maximos, Katsamanis, Athanasios, Diamantaras, Konstantinos, Katsouros, Vassilis, Fotinea, Evita, Panga, Depy, and Loupi, Dimitra

Subjects
Artificial intelligence, Automatic Speech Recognition, Subtitles alignment, Theatre, LSTM
Abstract

This paper presents a system that automates activation of events that improve the accessibility and enhance the experience in theatrical performances in real time and proposes and evaluates the core method employed therein. This method aligns a given set of subtitles that is created and synchronized by experts for a given “rehearsal” audio stream, to a new “performance” audio stream in real-time. The performance stream may be different from the rehearsal not only in terms of timing, but also in terms of spoken content. The method is built around an Automatic Speech Recognition System (ASR) that captures the performance stream in real-time and generates suggestions for subtitle timing corrections. The system is evaluated in an experimental performance with 35 participants while the core method is analysed independently, using sixteen artificial rehearsal-performance pair scenarios. Objective and subjective evaluation reveals specific directions for improvements for the core method. The investigation of the system shows that this innovative approach is promising for improving accessibility and the enhancement of the theatrical experience of audiences.

Published
2022

34. Towards a DHH Accessible Theater: Real-Time Synchronization of Subtitles and Sign Language Videos with ASR and NLP Solutions

Author

Bastas, Grigoris, Kaliakatsos-Papakostas, Maximos, Paraskevopoulos, Grigorios, Kaplanoglou, Pantelis, Christantonis, Konstantinos, Tsioustas, Charalampos, Mastrogiannopoulos, Dimitris, Panga, Depy, Fotinea, Evita, Katsamanis, Athanasios, Katsouros, Vassilis, Diamantaras, Konstantinos, and Maragos, Petros

Abstract

Despite the fact that in some areas of cultural life, as in the case of certain online video platforms or TV programs, notable progress has been made to provide content accessible to Deaf and Hard of Hearing people (DHH), the same cannot be said for live theater performances. In this work, a system called NLP-Theatre is presented, with the emphasis given to its technical aspects relevant to the problem of DHH inclusivity. The leading concept behind this system’s functioning is the automatic real-time extraction of the actors’ speech transcripts and their matching to a pre-arranged set of subtitles that correspond to the theater script at hand. The matching accuracy scores indicate the correct timings for the display of each subtitle to the audience during a live performance and the concurrent activation of several other events that render possible the synchronization of multiple streams of information directed towards the spectators. One such stream is also the video of sign language interpretation that is also synchronized in real-time with the live performance and mainly targets DHH individuals. A subjective evaluation of the subtitles and sign language streams is presented based on questionnaire responses provided by the guests in an experimental theater show. Furthermore, purely technical experiments were run, centered around the performance assessment of the Speech-To-Text (STT) alternatives that are employed in the system, that is both a commercial general-purpose remote solution and a custom ASR service. The latter employs a script-specific TDNN-HMM model that runs locally and is created using a Language Model (LM) adaptation strategy.

Published
2022
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35. A Machine Learning Approach for MIDI to Guitar Tablature Conversion

Author

Kaliakatsos-Papakostas, Maximos, Bastas, Grigoris, Makris, Dimos, Herremans, Dorrien, Katsouros, Vassilis, and Maragos, Petros

Abstract

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 history of guitar playing, specific chord fingerings have been developed across different musical styles that facilitate common idiomatic voicing combinations and motion between them. This paper presents a method for assigning guitar tablature notation to a given MIDI-based musical part (possibly consisting of multiple polyphonic tracks), i.e. no information about guitar-idiomatic expressional characteristics is involved (e.g. bending etc.) The current strategy is based on machine learning and requires a basic assumption about how much fingers can stretch on a fretboard; only standard 6-string guitar tuning is examined. The proposed method also examines the transcription of music pieces that was not meant to be played or could not possibly be played by a guitar (e.g. potentially a symphonic orchestra part), employing a rudimentary method for augmenting musical information and training/testing the system with artificial data. The results present interesting aspects about what the system can achieve when trained on the initial and augmented dataset, showing that the training with augmented data improves the performance even in simple, e.g. monophonic, cases. Results also indicate weaknesses and lead to useful conclusions about possible improvements.

Published
2022
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36. Towards a DHH Accessible Theater: Real-Time Synchronization of Subtitles and Sign Language Videos with ASR and NLP Solutions

Author

Bastas, Grigoris, primary, Kaliakatsos-Papakostas, Maximos, additional, Paraskevopoulos, Georgios, additional, Kaplanoglou, Pantelis, additional, Christantonis, Konstantinos, additional, Tsioustas, Charalampos, additional, Mastrogiannopoulos, Dimitris, additional, Panga, Depy, additional, Fotinea, Evita, additional, Katsamanis, Athanasios, additional, Katsouros, Vassilis, additional, Diamantaras, Konstantinos, additional, and Maragos, Petros, additional

Published
2022
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43. Innovative Applications of Natural Language Processing and Digital Media in Theatre and Performing Arts

Author

Chioustas, Charalampos, Petratou, Daphne, Kaliakatsos-Papakostas, Maximos, Katsouros, Vassilis, Kastritsis, Apostolos, Christantonis, Konstantinos, Diamantaras, Konstantinos, and Loupis, Michael

Subjects
Digital Art, Immersive Theatre, C63, ddc:330, Virtual Characters, Natural Language Processing
Abstract

The objective of our research is to investigate new digital techniques and tools, offering the audience innovative, attractive, enhanced and accessible experiences. The project focuses on performing arts, particularly theatre, aiming at designing, implementing, experimenting and evaluating technologies and tools that expand the semiotic code of a performance by offering new opportunities and aesthetic means in stage art and by introducing parallel accessible narrative flows. In our novel paradigm, modern technologies emphasize the stage elements providing a multilevel, intense and immersive theatrical experience. Moreover, lighting, video projections, audio clips and digital characters are incorporated, bringing unique aesthetic features. We also attempt to remove sensory and language barriers faced by some audiences. Accessibility features consist of subtitles, sign language and audio description. The project emphasises on natural language processing technologies, embedded communication and multimodal interaction to monitor automatically the time flow of a performance. Based on this, pre-designed and directed stage elements are being mapped to appropriate parts of the script and activated automatically by using the virtual "world" and appropriate sensors, while accessibility flows are dynamically synchronized with the stage action. The tools above are currently adapted within two experimental theatrical plays for validation purposes. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Published
2020
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