Research on Srikanth Cherla

Attending ISMIR 2019

Tue, 08 Oct 2019 00:00:00 +0000

Four of us from Moodagent — Reinier de Valk, Pierre Lafitte, Tomas Gajarsky, and myself — are attending ISMIR 2019 in Delft, Netherlands.

Two presentations from the team:

Reinier de Valk: “JosquIntab: A Dataset for Content-based Computational Analysis of Music in Lute Tablature” (main conference)
Tomas Gajarsky: “Reinforcement Learning Recommender System for Modelling Listening Sessions” (late-breaking session)

Come find us at the posters if you’re attending.

Remote Talk at Event Organised by Music Tech Community – India

Mon, 07 Jan 2019 00:00:00 +0000

I was invited by Music Tech Community – India to speak at an event in Bengaluru on December 29th, focused on “Machine Learning for Art & Music Generation.” Since Nina and I were on holiday in Mararikulam, Kerala at the time, I delivered it remotely via Skype.

The organisers — Albin Correya, Manaswi Mishra, and Siddharth Bharadwaj — made sure everything ran smoothly. I didn’t want to miss the opportunity, and it worked out well. Other speakers included Harshit Agarwal and two of the organisers.

Oral Presentation at ISMIR 2018

Wed, 10 Oct 2018 00:00:00 +0000

I attended ISMIR 2018 in Paris with colleagues from Jukedeck to present our StructureNet paper. This year’s format gave every accepted paper both an oral and a poster slot — a nice change from the traditional division.

I gave the oral presentation; colleagues Gabriele and Marco handled the poster. The talk was recorded and is on YouTube. Gabriele also wrote up a post on the Jukedeck R&D Team’s Medium page.

A highlight was the jam session organised by Uri Nieto from Pandora — I played bass with fellow attendees on two songs, alongside music ranging from AI-composed pieces to jazz, blues, rock, and heavy metal. I also played guitar for a cover of Blackest Eyes by Porcupine Tree, recorded on the boat cruise on the Seine. Also great to reconnect with people from the Music Informatics Research Group at City and to finally get a photo with both my master’s supervisor Hendrik and my PhD supervisor Tillman.

Paper Accepted at ISMIR 2018

Fri, 25 May 2018 00:00:00 +0000

A paper I co-authored with colleagues from Jukedeck has been accepted at ISMIR 2018 in Paris. Congratulations to Gabriele, Katerina, Matt, Samer, Marco, Ed, and Kevin.

Medeot G., Cherla S., Kosta K., McVicar M., Abdallah S., Selvi M., Newton-Rex E., Webster K. “StructureNet: Inducing Structure in Generated Melodies.” Proc. ISMIR 2018. Paris, France.

(Automated) Curriculum Learning

Sat, 18 Nov 2017 00:00:00 +0000

I’ve lately spent some time reading about Curriculum Learning and experimenting with the algorithms described in two of the papers in this domain Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009, June). Curriculum learning. In Proceedings of the 26th annual international conference on machine learning (pp. 41-48). ACM. Graves, A., Bellemare, M. G., Menick, J., Munos, R., & Kavukcuoglu, K. (2017). Automated Curriculum Learning for Neural Networks. arXiv preprint arXiv:1704.03003. The first of the above can be considered important given how with empirical results supporting Curriculum Learning, it revived the interest among researchers in this technique. The second is one of the recently proposed approaches for Curriculum Learning that I thought would be interesting to understand in greater depth. I’ve summarised my thoughts on these in a short presentation. I hope to share my code and results not too long from now as well.

Invited Talks at IIIT-Bangalore and Robert Bosch

Wed, 13 Sep 2017 00:00:00 +0000

I’m on a break from Jukedeck until September 22nd, visiting Bangalore. Past mentors invited me to present at two organisations.

Today I gave a talk at IIIT-Bangalore covering my PhD work on sequence modelling in music — RBMs and Recurrent RBMs. A similar talk is scheduled at Robert Bosch on September 18th. Slides are available as a PDF if anyone is interested.

Participating in CSMC 2017 Panel Discussion

Sat, 02 Sep 2017 00:00:00 +0000

I’ll be participating in a panel discussion titled “Applying Musical Patterns in Generation” at the 2nd Conference on Computer Simulation of Musical Creativity (CSMC), September 11–13, 2017, in Milton Keynes, UK.

Fellow panellists: Elaine Chew, Roger Dean, Steven Jan, David Meredith, and Tillman Weyde. Organised by Iris Yuping Ren. Looking forward to it.

Breaking Down the Differentiable Neural Computer

Wed, 24 May 2017 00:00:00 +0000

My PhD work on RNNs for musical sequence prediction got me interested in memory-augmented neural architectures. I spent a couple of weeks working through two key papers:

Neural Turing Machine — Graves et al., Google DeepMind (arXiv)
Differentiable Neural Computer — a more advanced variant published in Nature

I put together a Google Slides presentation with my observations and notes. Feedback welcome — let me know if anything needs correcting.

Music and Connectionism

Mon, 01 Aug 2016 00:00:00 +0000

The many contributions made during the past three decades to computer-assisted analysis and generation of music with the aid of Connectionist architectures can be seen to have occured in two waves, in parallel with developments in Connectionist research itself. During the first wave, the founding principles of Connectionism were introduced (Rumelhart et al., 1986) through the idea of Parallel Distributed Processing according to which mental phenomena occur as a result of simultaneous interactions between simple elementary processing units, as opposed to the then prevailing notion of Sequential Symbolic Processing which explained the same phenomena in terms of sequential interactions between complex goal-specific units. Its significance is largely theoretical, with a few experimental and empirical results to support the feasibility of the theory. Following several years of reduced interest, the second wave further strengthened the claims made by its precursor through a series of successful high-impact real-world applications. This was owing to both the proposal of newer theories, and the availability of greater computational power and vast amounts of data that enabled the demonstration of the efficacy of these theories nearly two decades on (Bengio, 2009; LeCun et al.,2012). The innovations that came about as a result of these two phases trickled down to several application domains (Krizhevsky et al., 2012; Hinton et al., 2012;Collobert et al., 2011) of which music is one (Todd and Loy, 1991; Griffith and Todd,1999; Humphrey et al., 2012). This section reviews notable contributions among the many that demonstrated the application of connectionism to symbolic music modelling during these two waves in order to present a historical perspective together with an overview of the techniques employed.

Oral Presentation at IJCNN 2015

Mon, 20 Jul 2015 00:00:00 +0000

My paper was accepted for oral presentation at the 28th International Joint Conference on Neural Networks in Killarney, Ireland.

“Discriminative Learning and Inference in the Recurrent Temporal RBM for Melody Modelling” — The paper proposes the Recurrent Temporal Discriminative RBM (RTDRBM), which combines discriminative learning with recurrent structure to predict probability distributions for the next note in a melody. Evaluated on 8 folk and chorale melody datasets, it outperforms n-grams and other connectionist models with statistically significant improvements.

Oral Presentation at ISMIR 2014

Tue, 04 Nov 2014 00:00:00 +0000

I attended ISMIR 2014 in Taipei with two accepted papers:

“Multiple Viewpoint Melodic Prediction with Fixed-Context Neural Networks” — Continuing earlier work on neural network–based note prediction using the multiple viewpoints representation, an event-based representation of symbolic music data.

“An RNN-based Music Language Model for Improving Automatic Music Transcription” — Co-authored with Siddharth Sigtia and Emmanouil Benetos.

Both presented as posters. Prof. Ye Wang’s keynote on music’s therapeutic applications in rehabilitation was a highlight — it made me think about how melody modelling research could eventually serve therapeutic purposes.

Poster Presentation at the Machine Learning Summer School

Fri, 09 May 2014 00:00:00 +0000

I was selected to attend the Machine Learning Summer School in Reykjavik (April 25–May 4, 2014) and received a travel grant. I presented a poster on musical pitch prediction with neural networks.

Particularly valuable tutorials: Machine Learning and HCI (Roderick Murray-Smith), Introduction to ML (Neil Lawrence), Deep Learning (Yoshua Bengio). The reinforcement learning content was especially interesting — I could see potential applications in my music modelling work.

Outside the summer school: the Golden Circle Tour, a hike up Mount Esjan, and a last-minute trip to the Blue Lagoon before flying home. Reykjavik is one of the most unique places I’ve visited.

Oral Presentation at ISMIR 2013

Sun, 17 Nov 2013 00:00:00 +0000

I presented “A Distributed Model for Multiple Viewpoint Melodic Prediction” at ISMIR 2013 in Curitiba, Brazil. The paper uses a Restricted Boltzmann Machine to model melodic sequences — demonstrating that the model can make use of information in longer contexts more effectively than n-gram models. It won the Best Student Paper Award.

I also organised a late-breaking session on MIR applications in educational settings.

After the conference I spent a week in Rio de Janeiro — Cristo Redentor, Sugarloaf Mountain, beaches near Copacabana. My supervisor and a colleague came along for sightseeing. A fabulous experience.

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

Tue, 24 Sep 2013 00:00:00 +0000

Two papers accepted at MML 2013, co-located with the European Conference on Machine Learning.

“A Neural Probabilistic Model for Predicting Melodic Sequences” — Uses RBMs for folk melody classification, outperforming n-gram models, with linear scaling with context length.

“An Efficient Shift-Invariant Model for Polyphonic Music Transcription” — Co-authored with Emmanouil Benetos.

I couldn’t attend, so Emmanouil presented both. Slides and PDFs available on request.

Downloads

MML 2013 Paper MML 2013 Presentation MML 2013 Paper

Poster Presentation at the 3rd Annual Researchers' Symposium

Thu, 27 Jun 2013 00:00:00 +0000

I presented “A Neural Network for Predicting Musical Pitch” at City University London’s 3rd Annual Researchers’ Symposium — an event where doctoral students present their research to non-specialist audiences.

The work uses RBMs to predict sequences of musical pitch from monophonic MIDI melodies, outperforming n-gram models while requiring fewer parameters. The poster won the Best Poster Presentation Award.

Poster created in Beamer/LaTeX — PDF available on request.

City University 3rd Annual Researchers’ Symposium Poster

Oral Presentation at the 5th BCS Doctoral Consortium

Fri, 17 May 2013 00:00:00 +0000

My abstract was accepted for the BCS 5th Doctoral Consortium and I presented on May 16, 2013.

“A Neural Probabilistic Model for Music Prediction” — Proposes using a Restricted Boltzmann Machine to model musical pitch sequences in monophonic MIDI melodies, addressing the context limitations of Markov models. The model can make use of information in longer sequences more effectively than recently evaluated Markov models, with potential applications in music education, composition, transcription, and classification.