My research efforts are focused mostly around tackling of music information retrieval tasks with deep learning methods. Specifically, approaching the problem of polyphonic music transcription as a multi-class classification problem, I train neural network models to recognize notes in musical audio signal.
My broader interests include:
- deep learning
- neural network architectures
- optimization and learning algorithms
- unsupervised learning and generative modelling
- approximate inference and graphical models
- reinforcement learning
- signal processing methods for music informatics
- I attended Transylvanian Machine Learning Summer School TMLSS2018 in Cluj-Napoca and won Best Poster award for presenting my work during the poster sessions. My notes might come as a separate post soon. In the meantime, you can read more about the event in this outstanding reportÂ written directly by event organizers.
- I just released my own Implementation of Distilling Binary Soft Decision Tree according to the paper by Frosst & Hinton. Check it out!
- I attended Deep Learning Summer School DeepLearn2017 in Bilbao, Spain. Here are some of my notes from this exciting event:Â https://goo.gl/btM4Fv
Authors: LukÃ¡Å¡ MartÃ¡k, MÃ¡rius Å ajgalÃk, Wanda BeneÅ¡ovÃ¡
We exploit modelling capacity of deep end-to-end neural network architecture called WaveNet, to perform a multi-class multi-label classification of frames of polyphonic music into sets of notes present in the polyphony. The approach is evaluated on theÂ MusicNet benchmark dataset for deep learning approaches to music transcription.
Modelling Music Structure using Artificial Neural Networks
Authors: LukÃ¡Å¡ MartÃ¡k, MÃ¡rius Å ajgalÃk
My Master Thesis takes multiple approaches to the task of polyphonic note transcription. The project compares various neural network architectures used for modelling of polyphonic textures in musical audio data. This project was also a basis for the subsequent work focused on raw audio modelling with WaveNet which was also later published as a separate project.
- Martak, L. S., Sajgalik, M., & Benesova, W. (2018). Polyphonic Note Transcription of Time-Domain Audio Signal with Deep WaveNet Architecture. 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), 1â€“5. https://doi.org/10.1109/IWSSIP.2018.8439708
- BeÅˆuÅ¡, Å ., Trnka, M., Kuric, E., MartÃ¡k, L., Gravano, A., ComputaciÃ³n, D. De, & Aires, U. D. B. (2018). Prosodic entrainment and trust in human-computer interaction.Â Proceedings of International Conference Speech Prosody 2018, (June), 220â€“224. https://doi.org/10.21437/SpeechProsody.2018-45
- Modelling Music Structure using Artificial Neural Networks.
MartÃ¡k, LukÃ¡Å¡; Å ajgalÃk, MÃ¡rius. Diploma thesis. Bratislava: FIIT STU, 2016.
- Neural Networks (Autumn 2018)
- Computer VisionÂ (Spring 2018)
- Digital Signal Processing, Graphics and Multimedia (Autumn 2017)
- Neural NetworksÂ (Autumn 2017)