3 citations · 6 across the 13 of their papers we have counts for
13 papers
Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation
Naman Garg, Sarika Jain, George Fazekas
We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a multi-modal and personalized co…
LiveBand: Live Accompaniment Generation in the Audio Domain
Marco Pasini, Javier Nistal, Ben Hayes +3
We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints. Our method trains a causal tran…
Diffusion Timbre Transfer Via Mutual Information Guided Inpainting
Ching Ho Lee, Javier Nistal, Stefan Lattner +2
We study timbre transfer as an inference-time editing problem for music audio. Starting from a strong pre-trained latent diffusion model, we introduce a lightweight procedure that…
CoDiCodec: Unifying Continuous and Discrete Compressed Representations of Audio
Marco Pasini, Stefan Lattner, George Fazekas
Efficiently representing audio signals in a compressed latent space is critical for latent generative modelling. However, existing autoencoders often force a choice between continu…
Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks
Christos Plachouras, Julien Guinot, George Fazekas +3
Downstream probing has been the dominant method for evaluating model representations, an important process given the increasing prominence of self-supervised learning and foundatio…
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding
Marco Pasini, Stefan Lattner, George Fazekas
Efficiently compressing high-dimensional audio signals into a compact and informative latent space is crucial for various tasks, including generative modeling and music information…
Continuous Autoregressive Models with Noise Augmentation Avoid Error Accumulation
Marco Pasini, Javier Nistal, Stefan Lattner +1
Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive m…
Diff-MSTC: A Mixing Style Transfer Prototype for Cubase
Soumya Sai Vanka, Lennart Hannink, Jean-Baptiste Rolland +1
In our demo, participants are invited to explore the Diff-MSTC prototype, which integrates the Diff-MST model into Steinberg's digital audio workstation (DAW), Cubase. Diff-MST, a…
Music2Latent: Consistency Autoencoders for Latent Audio Compression
Marco Pasini, Stefan Lattner, George Fazekas
Efficient audio representations in a compressed continuous latent space are critical for generative audio modeling and Music Information Retrieval (MIR) tasks. However, some existi…
MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models
Benno Weck, Ilaria Manco, Emmanouil Benetos +3
Multimodal models that jointly process audio and language hold great promise in audio understanding and are increasingly being adopted in the music domain. By allowing users to que…
Semi-Supervised Contrastive Learning of Musical Representations
Julien Guinot, Elio Quinton, György Fazekas
Despite the success of contrastive learning in Music Information Retrieval, the inherent ambiguity of contrastive self-supervision presents a challenge. Relying solely on augmentat…
Diff-MST: Differentiable Mixing Style Transfer
Soumya Sai Vanka, Christian Steinmetz, Jean-Baptiste Rolland +2
Mixing style transfer automates the generation of a multitrack mix for a given set of tracks by inferring production attributes from a reference song. However, existing systems for…
Self-Supervised Music Source Separation Using Vector-Quantized Source Category Estimates
Marco Pasini, Stefan Lattner, George Fazekas
Music source separation is focused on extracting distinct sonic elements from composite tracks. Historically, many methods have been grounded in supervised learning, necessitating…