Oral Presentation at the 14th International Society for Music Information Retrieval Conference

Having had one paper accepted at the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), I travelled to Brazil for two weeks where I was in Curitiba first for a week where the conference was being held and then in Rio for the rest of the time on a holiday. ISMIR is the leading conference when it comes to research in Music Information Retrieval and other related topics in Music Technology. The paper I presented there was titled, “A Distributed Model for Multiple Viewpoint Melodic Prediction”. Its abstract is the following:

“The analysis of sequences is important for extracting information from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for melodic sequences. The model is similar to a previous successful neural network model for natural language. It is first trained to predict the next pitch in a given pitch sequence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. In our evaluation, this RBM-based prediction model performs slightly better than previously evaluated n-gram models in most cases. Results on a corpus of chorale and folk melodies showed that it is able to make use of information present in longer contexts more effectively than n-gram models, while scaling linearly in the number of free parameters required.”

Welcome to ISMIR 2013
Welcome to ISMIR 2013

The paper was chosen for an oral presentation, and it also won a Best Student Paper Award at the conference. On the final day of the conference, I also organised a late-breaking session on “MIR in Music Education” which is a topic I am very interested in, and also participated in several other sessions organised by others.

Receiving my Best Student Paper prize with other prize recipients.
Receiving my Best Student Paper prize with other prize recipients.

I also met a very interesting guy named Anderson during my stay at the Knock Knock hostel in Curitiba, who is also a PhD student doing his research on Armadillos!

A very good friend I made during my stay at the Knock Knock Hostel in Curitiba
A very good friend I made during my stay at the Knock Knock Hostel in Curitiba

Then I travelled to Rio de Janeiro for a week where I lived in a hostel located just a few minutes away from Copacabana beach. I spent my time there hanging out at the many beaches, and visiting iconic landmarks such as Cristo Redentor and Sugarloaf mountain among other places recommended to me by the locals I met in the hostel, and also taking a bus tour with some other tourists.

Something very unusual I noticed at Escadaria Selaron!
Something very unusual I noticed at Escadaria Selaron!

I was also joined there by my supervisor Tillman, and my friend and colleage Reinier who accompanied me during some site-seeing.

Tillman and Reinier with a bust of Heitor Villa-Lobos
Tillman and Reinier with a bust of Heitor Villa-Lobos

In all this was a fabulous experience and I thoroughly enjoyed my time in Brazil! I’m sharing a copy of my paper and presentation slides below.

ISMIR 2013 Paper presentation-ismir-2013

ISMIR 2013 Presentation

Oral Presentation at the 6th International Workshop on Machine Learning and Music

My paper was accepted at the 6th International Workshop on Machine Learning and Music (MML 2013) held in conjunction with the European Conference on Machine Learning (ECML 2013) this year. The paper is titled, “A Neural Probabilistic Model for Predicting Melodic Sequences”. Its abstract is the following:

“We present an approach for modelling melodic sequences using Restricted Boltzmann Machines, with an application to folk melody classification. Results show that this model’s predictive performance is slightly better in our experiment than that of previously evaluated n-gram models. The model has a simple structure and in our evaluation it scaled linearly in the number of free parameters with length of the modelled context. A set of these models is used to classify 7 different styles of folk melodies with an accuracy of 61.74%.”

Unfortunately, I was unable to go to the conference myself so my colleague Emmanouil presented the paper on my behalf. In addition to this, another paper I co-authored with Emmanouil, titled “An Efficient Shift-Invariant Model for Polyphonic Music Transcription” was also accepted into the same workshop which he presented as well.

I’m including copies of the paper and the presentation below.

MML 2013 Presentation

MML 2013 Paper

Poster Presentation at the 3rd Annual Researchers’ Symposium

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.

City University 3rd Annual Researchers’ Symposium Poster

Oral Presentation at the 5th BCS Doctoral Consortium

My abstract was selected at the British Computer Society’s 5th Doctoral Consortium and I made a presentation, titled “A Neural Probabilistic Model for Music Prediction” on May 16, 2013. The abstract for the talk is the following:

“Neural Networks and Markov models have received long-standing attention in music prediction. 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 when the sequences become longer. We present a new model for music prediction based on the Restricted Boltzmann Machine (RBM) and evaluate it 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 recently evaluated Markov models, outperforming them on the said corpus while also scaling gracefully in the required number of free parameters. While initial results have been encouraging, there is also scope for considering more powerful models that build upon this basic architecture. Some questions that hope to be addressed in the future are whether such models can provide an insight into the development of musical taste, make predictions about more complex musical structures that involve polyphony and variations in rhythm, be of use in music education and compositional assistance, and aid in Music Information Retrieval tasks such as music transcription and classification.”

I have also attached a PDF copy of the presentation (all made in Beamer/LaTeX) below.

BCS Doctoral Consortium Presentation Slides