Best paper award at CMMR 2025

The article "Leveraging Melodic Context for Improved Svara Representation" by Thomas Nuttall, Vivek Vijayan, Xavier Serra, and Lara Pearson has received the best paper award at the 17th International Symposium on Computer Music Multidisciplinary Research.

Abstract:

For the South Indian musical tradition known as Carnatic music, embeddings of svara (note) pitch time series have proven useful for tasks such as svara classification and performance analysis. In this paper, we extend an existing embedding method by incorporating findings from musicological research on the relationship between the performance of a svara and its immediate melodic context, in order to improve the learning of these embedding models. We present a context-aware GRU-based model, adapting the existing DeepGRU architecture to encode both svara and its surrounding melodic context, before combining them via a co-attention mechanism prior to classification. For a ground truth dataset of 2,077 expert svara annotations across two performances in rāga Bhairavi, we observe that the inclusion of melodic context leads to a 6.6% absolute increase in F1 score for svara label classification (from 78.3% to 84.9%), and an 7.8% absolute increase (from 59.9% to 67.7%) for classification of svara-form: sub-svara clusters that capture gamaka (ornamentation) variations in the performed svara.

Dolby Barcelona Scientific Paper Award 2025

Guillem Cortès Sebastià, Research Engineer at BMAT (a music innovation company based in Barcelona) and recent PhD graduate at the Music Technology Group, Universitat Pompeu Fabra is the winner of the Dolby Barcelona Scientific Paper Award 2025. Guillem is recognized for the paper: “PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching,” by Guillem Cortès-Sebastià, Benjamin Martin, Emilio Molina, Xavier Serra, Romain Hennequin, presented at ISMIR 2025.

Abstract:

This work introduces PeakNetFP, the first neural audio fingerprinting (AFP) system designed specifically around spectral peaks. This novel system is designed to leverage the sparse spectral coordinates typically computed by traditional peak-based AFP methods. PeakNetFP performs hierarchical point feature extraction techniques similar to the computer vision model PointNet++, and is trained using contrastive learning like in the state-of-the-art deep learning AFP, NeuralFP. This combination allows PeakNetFP to outperform conventional AFP systems and achieves comparable performance to NeuralFP when handling challenging time-stretched audio data. In extensive evaluation, PeakNetFP maintains a Top-1 hit rate of over 90% for stretching factors ranging from 50% to 200%. Moreover, PeakNetFP offers significant efficiency advantages: compared to NeuralFP, it has 100 times fewer parameters and uses 11 times smaller input data. These features make PeakNetFP a lightweight and efficient solution for AFP tasks where time stretching is involved. Overall, this system represents a promising direction for future AFP technologies, as it successfully merges the lightweight nature of peak-based AFP with the adaptability and pattern recognition capabilities of neural network-based approaches, paving the way for more scalable and efficient solutions in the field.

Congratulations!!