Nineteen new theses, carried out and finished during the academic year 2024-2025 in the Master in Sound and Music Computing, are now available:

• Avalos, Salvador, Ada: Freesound Loop Generator

• Alexandrovich Danilin, Danila: Interactive machine learning for music classification

• Bosma, Justin: Generating Abstract Rhythm Streams

• Cárdenas Gracia, Sergio: Comparison of Audio Encoders for Audio-Text Contrastive Learning Representations 

• Doerfler, Robin: Neural Engine Sound Synthesis with Physics-Informed Inductive Biases and Differentiable Signal Processing

• Fuhrmann, Théo: Understanding Audio Source Separation in Carnatic Music with Multimodal Data

• Jaideep, Madhav: Research and Evaluation of Automatic Sound FX Classification in Freesound using the Universal Category System

• Lallana Babiloni, Manuel: InScoreAI: Collaborative Score Inpainting with Anticipatory Transformers

• Lissenko, Tanguy: SurpriseLSTM: Neural Modeling of Musical Expectation and Surprise in Monophonic Melodies

• Marcé Forns, Joaquim: Visualization of the output of Sound Event Detection algorithms in Freesound

• Mishra, Anmol: The Essence Remains the Same: Generative Modeling of Expressive Percussion

• Monsalve Fernández, Ángel: Improving the Semantic Structure of Neural Audio Codecs

• Oktay, Isabelle: MuSA: A New TEL Platform for Enhancing Self-Reflection and Musical Understanding through Saliency Analysis of Performance Recordings

• Padoa, Jed: Semantic Control Over Neurally Synthesized Audio via Latent Disentanglement

• Prabhu, Satyajeet: Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches

• Schweinitz, Serafin: A Fine Tuning Strategy to Improve Musical Source Separation Quality for Indian Carnatic Music

• Scutari, Tito: Extracting sonic Trajectories

• Vilanova, Alexandre: Real-time Generation of Percussive Rhythms Using Descriptors

• Yapici, Tolga: Open-Domain Zero-Shot Audio Tagging: Evaluation via Semantic Embeddings

Supported by:

Cátedra UPF-BMAT en Inteligencia Articial y Música (TSI-100929-2023-1). Project funded by Secretaría de Estado de Digitalización e Inteligencia Artificial, the European Union-Next Generation EU, and by BMAT Music Innovators, the Music Operating System