publications

2023

1. 

   TapTamDrum: A Dataset for Dualized Drum Patterns

   Behzad Haki, Błażej Kotowski, Cheuk Lee, and 1 more author

   Nov 2023

   Abs Bib PDF Code Poster Slides Website

   Drummers spend extensive time practicing rudiments to develop technique, speed, coordination, and phrasing. These rudiments are often practiced on "silent" practice pads using only the hands. Additionally, many percussive instruments across cultures are played exclusively with the hands. Building on these concepts and inspired by Einstein’s probably apocryphal quote, "Make everything as simple as possible, but not simpler," we hypothesize that a dual-voice reduction could serve as a natural and meaningful compressed representation of multi-voiced drum patterns. This representation would retain more information than its corresponding monotonic representation while maintaining relative simplicity for tasks such as rhythm analysis and generation. To validate this potential representation, we investigate whether experienced drummers can consistently represent and reproduce the rhythmic essence of a given drum pattern using only their two hands. We present TapTamDrum: a novel dataset of repeated dualizations from four experienced drummers, along with preliminary analysis and tools for further exploration of the data.

   @inproceedings{Haki2023TapTamDrum,
     title = {{TapTamDrum: A Dataset for Dualized Drum Patterns}},
     author = {Haki, Behzad and Kotowski, Błażej and Lee, Cheuk and Jorda, Sergi},
     booktitle = {{Proceedings of the 24th International Society for Music Information Retrieval Conference}},
     year = {2023},
     month = nov,
     publisher = {ISMIR},
   }

2. 

   NeuralMidiFx: A Wrapper Template for Deploying Neural Networks as VST3 Plugins

   Behzad Haki, Julian Lenz, and Sergi Jorda

   Sep 2023

   Abs Bib PDF Code Slides

   Proper research, development and evaluation of AI-based generative systems of music that focus on performance or composition require active user-system interactions. To include a diverse group of users that can properly engage with a given system, researchers should provide easy access to their developed systems. Given that many users (i.e. musicians) are non-technical to the field of AI and the development frameworks involved, the researchers should aim to make their systems accessible within the environments commonly used in production/composition workflows (e.g. in the form of plugins hosted in digital audio workstations). Unfortunately, deploying generative systems in this manner is highly expensive. As such, researchers with limited resources are often unable to provide easy access to their works, and subsequently, are not able to properly evaluate and encourage active engagement with their systems. Facing these limitations, we have been working on a solution that allows for easy, effective and accessible deployment of generative systems. To this end, we propose a wrapper/template called NeuralMidiFx, which streamlines the deployment of neural network based symbolic music generation systems as VST3 plugins. The proposed wrapper is intended to allow researchers to develop plugins with ease while requiring minimal familiarity with plugin development.

   @inproceedings{Haki2023NeuralMidiFx,
     author = {Haki, Behzad and Lenz, Julian and Jorda, Sergi},
     booktitle = {{Proceedings of the 4th International Conference on on AI and Musical Creativity}},
     publisher = {},
     title = {{NeuralMidiFx: A Wrapper Template for Deploying Neural Networks as VST3 Plugins}},
     year = {2023},
     month = sep,
   }

3. 

   Completing Audio Drum Loops with Symbolic Drum Suggestions

   Behzad Haki, Teresa Pelinski, Marina Nieto, and 1 more author

   Apr 2023

   Abs Bib PDF Code Slides Website

   Sampled drums can be used as an affordable way of creating human-like drum tracks, or perhaps more interestingly, can be used as a mean of experimentation with rhythm and groove. Similarly, AI-based drum generation tools can focus on creating human-like drum patterns, or alternatively, focus on providing producers/musicians with means of experimentation with rhythm. In this work, we aimed to explore the latter approach. To this end, we present a suite of Transformer-based models aimed at completing audio drum loops with stylistically consistent symbolic drum events. Our proposed models rely on a reduced spectral representation of the drum loop, striking a balance between a raw audio recording and an exact symbolic transcription. Using a number of objective evaluations, we explore the validity of our approach and identify several challenges that need to be further studied in future iterations of this work. Lastly, we provide a real-time VST plugin that allows musicians/producers to utilize the models in real-time production settings.

   @inproceedings{Haki2023Completing,
     author = {Haki, Behzad and Pelinski, Teresa and Nieto, Marina and Jorda, Sergi},
     booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression (NIME) 2023},
     year = {2023},
     month = apr,
     publisher = {NIME},
     title = {{Completing Audio Drum Loops with Symbolic Drum Suggestions}},
   }

2022

1. 

   Real-Time Drum Accompaniment Using Transformer Architecture

   Behzad Haki, Marina Nieto, Teresa Pelinski, and 1 more author

   Sep 2022

   Abs Bib PDF Code Slides

   This paper presents a real-time drum generation system capable of accompanying a human instrumentalist. The drum generation model is a transformer encoder trained to predict a short drum pattern given a reduced rhythmic representation. We demonstrate that with certain design considerations, the short drum pattern generator can be used as a real-time accompaniment in musical sessions lasting much longer than the duration of the training samples. A discussion on the potentials, limitations and possible future continuations of this work is provided.

   @inproceedings{haki_behzad_2022_7088343,
     author = {Haki, Behzad and Nieto, Marina and Pelinski, Teresa and Jordà, Sergi},
     title = {{Real-Time Drum Accompaniment Using Transformer Architecture}},
     booktitle = {{Proceedings of the 3rd International Conference on on AI and Musical Creativity}},
     year = {2022},
     publisher = {AIMC},
     month = sep,
     doi = {10.5281/zenodo.7088343},
     url = {https://doi.org/10.5281/zenodo.7088343},
   }

2021

1. 

   Transformer Neural Networks for Automated Rhythm Generation

   Thomas Nuttall, Behzad Haki, and Sergi Jorda

   Jun 2021

   Abs Bib PDF Code Slides Website

   Recent applications of Transformer neural networks in the field of music have demonstrated their ability to effectively capture and emulate long-term dependencies characteristic of human notions of musicality and creative merit. We propose a novel approach to automated symbolic rhythm generation, where a Transformer-XL model trained on the Magenta Groove MIDI Dataset is used for the tasks of sequence generation and continuation. Hundreds of generations are evaluated using blind-listening tests to determine the extent to which the aspects of rhythm we understand to be valuable are learnt and reproduced. Our model is able to achieve a standard of rhythmic production comparable to human playing across arbitrarily long time periods and multiple playing styles.

   @inproceedings{NIME21_33,
     article-number = {33},
     author = {Nuttall, Thomas and Haki, Behzad and Jorda, Sergi},
     title = {Transformer Neural Networks for Automated Rhythm Generation},
     booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
     year = {2021},
     month = jun,
     address = {Shanghai, China},
     issn = {2220-4806},
     doi = {10.21428/92fbeb44.fe9a0d82},
     url = {https://nime.pubpub.org/pub/8947fhly},
     presentation-video = {https://youtu.be/Ul9s8qSMUgU},
   }

2019

1. 

   A Bassline Generation System Based on Sequence-to-Sequence Learning

   Behzad Haki, and Sergi Jorda

   Jun 2019

   Abs Bib PDF Code Website

   This paper presents a detailed explanation of a system generating basslines that are stylistically and rhythmically interlocked with a provided audio drum loop. The proposed system is based on a natural language processing technique: word-based sequence-to-sequence learning using LSTM units. The novelty of the proposed method lies in the fact that the system is not reliant on a voice-by-voice transcription of drums; instead, in this method, a drum representation is used as an input sequence from which a translated bassline is obtained at the output. The drum representation consists of fixed size sequences of onsets detected from a 2-bar audio drum loop in eight different frequency bands. The basslines generated by this method consist of pitched notes with different duration. The proposed system was trained on two distinct datasets compiled for this project by the authors. Each dataset contains a variety of 2-bar drum loops with annotated basslines from two different styles of dance music: House and Soca. A listening experiment designed based on the system revealed that the proposed system is capable of generating basslines that are interesting and are well rhythmically interlocked with the drum loops from which they were generated.

   @inproceedings{haki2019,
     author = {Haki, Behzad and Jorda, Sergi},
     title = {A Bassline Generation System Based on Sequence-to-Sequence Learning},
     pages = {204--209},
     booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
     editor = {Queiroz, Marcelo and Sedó, Anna Xambó},
     year = {2019},
     month = jun,
     publisher = {UFRGS},
     address = {Porto Alegre, Brazil},
     issn = {2220-4806},
     doi = {10.5281/zenodo.3672928},
   }