It’s just been confirmed that four of us from Moodagent – Reinier de Valk, Pierre Lafitte, Tomas Gajarsky and I, will be attending ISMIR 2019 in Delft (The Netherlands). This year, two of my colleagues from Moodagent will be presenting their work at ISMIR:
“The multiple viewpoints representation is an event-based representation of symbolic music data which offers a means for the analysis and generation of notated music. Previous work using this representation has predominantly relied on n-gram and variable order Markov models for music sequence modelling. Recently the efficacy of a class of distributed models, namely restricted Boltzmann machines, was demonstrated for this purpose. In this paper, we demonstrate the use of two neural network models which use fixed-length sequences of various viewpoint types as input to predict the pitch of the next note in the sequence. The predictive performance of each of these models is comparable to that of models previously evaluated on the same task. We then combine the predictions of individual models using an entropy-weighted combination scheme to improve the overall prediction performance, and compare this with the predictions of a single equivalent model which takes as input all the viewpoint types of each of the individual models in the combination.”
I have to note that this year’s ISMIR organisation was fantastic! Everything from the review process, information on the website to the venue, the assitance at the venue, and the banquet were very well managed and executed by the organisers. The most interesting part of the conference for me was the keynote lecture, titled “Sound and Music Computing for Exercise and (Re-)habilitation” by Prof. Ye Wang, in which he described the potential in music to serve as a means to rehabilitate and improve the quality of life of individuals with different ailments, and illustrated this with the help of a few projects his group at the National University of Singapore has been working. It was a very inspiring talk, and I really admire Dr. Wang’s statement regarding the often overlooked direct impact of research and published work to society which has been the cornerstone of these projects. I have lately taken interest in Music Therapy and have been going through some literature to see if my own work on music modelling can in some way be applied to achieve therapeutic goals. There were some interesting late-breaking sessions as well that I took part in, including the very successful one organised by my supervisor Tillman on Big Data and Music where I was taking notes during the discussion.
And finally, as is always the case when I attend a conference, I did take some time off in Taipei and its surrounding areas. On one evening, I joined some friends and colleagues to go see the tallest building in the city – Taipei 101.
On another day, a couple of us planned a day-trip to a nearby village called Jiufen where we checked out some temples, the market and the old Japanese mining village on top of a hill.
And on another day, I joined my buddy Marius on a local site-seeing round to see some local museums, Shilin night market, Chiang Kai Shek Memorial, and other places before taking the long flight back to London eventually.
Taipei was fantastic, and I’d be up for another visit anytime! Last but not least, the hospitality of Fun Taipei hostel made the whole trip a little better each day.
I was selected to attend the Machine Learning Summer School in Reykjavik between April 25-May 4, 2014. I was also awarded a travel grant to attend this event which made it possible for me to attend it. I also proposed to present a poster about my ongoing work on musical pitch prediction with neural networks.
Many of the topics were very new to me, but I found the tutorials on Machine Learning and HCI (Roderick Murray-Smith), Introduction to ML (Neil Lawrence), Deep Learning (Yoshua Bengio), Probabilistic Modelling (Iain Murray), and Reinforcement Learning (David Silver) particularly interesting. Especially the last talk seemed like there was much in it that could be adopted into my own work on music modelling and I was very tempted to do so. Let’s see how that goes.
I was also a bit stressed carrying out experiments for a paper we’re submitting to the 15th International Society of Music Information Retrieval Conference (ISMIR 2014). So fingers-crossed that it will all work out for that.
I managed to travel a little while I was in Reykjavik. This was something that had to be done given how novel a destination Iceland is. I joined the rest of the workshop attendees on the Golden Circle Tour that showed us some fascinating and very alien Icelandic landscapes.
And finally, I made a last-minute trip to the Blue Lagoon on the day before my return to London.
It was indeed very fortunate that I was able to attend the summer school in Reykjavik. This has been an incredible learning experience one of the most unique destinations I have been to in my entire life!
I’m sharing a copy of the poster (made using Beamer/LaTeX) I presented here.
The Researchers’ Symposium is an annual event held at City University London where doctoral students have the opportunity to showcase their ongoing research to a primarily non-technical audience. My abstract was selected this year for the 3rd Annual Researchers’ Symposium and I opted for presenting a poster here, titled “A Neural Network for Predicting Musical Pitch”. The abstract I submitted is the following:
“The analysis of sequential patterns is important for extracting information from music owing to its fundamentally temporal nature. Neural Networks and Markov models are two classes of models that have been considered frequently for predicting sequences of events in music. The latter, while being successful at modelling the joint probability of short musical sequences, suffer from problems pertaining to the curse of dimensionality and zero-occurrence as the sequences become longer. Here, we present a distributed model for music prediction based on a type of neural network called the Restricted Boltzmann Machine (RBM). We evaluate this model, first on sequences of musical pitch in a corpus of monophonic MIDI melodies. The results show that this model is able to make use of information present in longer sequences more effectively than previously evaluated n-gram models, outperforming them on the said corpus while also scaling gracefully in the number of free parameters required. As a case study, we also employ this model for classifying folk melodies according to origin.
Furthermore, a discussion on future extensions of the model will also be presented. Of relevance here is its extension to a larger structure known as a deep belief network which is capable of learning interesting features of data presented to it at multiple levels of abstraction. Given the encouraging results with the proposed model so far, its application to Music Information Retrieval tasks such as music transcription and segmentation will also be included in the discussion. Work is currently in progress to generalize this model for learning sequences of other musical dimensions such as note-duration, scale-degree, etc. for prediction.”
The poster I presented (created in Beamer/LaTeX) is also included below in this post. It won the Best Poster Presentation Award at the event.