16 accepted papers
Understanding what generative models retain from training data remains challenging, with implications for copyright and privacy. This question becomes particularly relevant as Rectified Flows power increasingly deployed systems. We analyze the interpolation path $X_\lambda = (1-\lambda)X_0 + \lambda…
Recently, self-supervised learning methods based on masked latent prediction have proven to encode input data into powerful representations. However, during training, the learned latent space can be further transformed to extract higher-level information that could be more suited for down-stream cla…
In this paper, we trace the evolution of Music Information Retrieval (MIR) over the past 25 years. While MIR gathers all kinds of research related to music informatics, a large part of it focuses on signal processing techniques for music data, fostering a close relationship with the IEEE Audio and A…
In this paper, we tackle the task of musical stem retrieval. Given a musical mix, it consists in retrieving a stem that would fit with it, i.e., that would sound pleasant if played together. To do so, we introduce a new method based on Joint-Embedding Predictive Architectures, where an encoder and a…
Tempo estimation is the task of estimating the periodicity of the dominant rhythm pulse of a music audio signal. It has therefore a close relationship with dominant pitch estimation. Recently, both tasks have been addressed in a Self-Supervised Learning (SSL) fashion so as to leverage unlabelled dat…
Blind Estimation of Audio Effects (BE-AFX) aims at estimating the audio effects (AFXs) applied to an original, unprocessed audio sample solely based on the processed audio sample. To train such a system traditional approaches optimize a loss between ground truth and estimated AFX parameters. This in…
Current state-of-the-art audio analysis systems rely on pre-trained embedding models, often used off-the-shelf as (frozen) feature extractors. Choosing the best one for a set of tasks is the subject of many recent publications. However, one aspect often overlooked in these works is the influence of…
In neural audio signal processing, pitch conditioning has been used to enhance the performance of synthesizers. However, jointly training pitch estimators and synthesizers is a challenge when using standard audio-to-audio reconstruction loss, leading to reliance on external pitch trackers. To addres…
Measuring noise in cities and automatically identifying the corresponding sound sources are a crucial challenge for policymakers. Indeed, such information helps addressing noise pollution and improving the well-being of urban dwellers. In recent years, researchers have provided annotated datasets re…
Due to their performances, deep neural networks have emerged as a major method in nearly all modern audio processing applications. Deep neural networks can be used to estimate some parameters or hyperparameters of a model, or in some cases the entire model in an end-to-end fashion. Although deep lea…
The use of a parameterized encoders or audio front-ends has shown promises in improving the interpretability of time domain single-channel source separation models such as Conv-TasNet. This type of filters also allows a potential reduction of the computational cost since larger encoder filters can b…
Automatic cover detection - the task of finding in an audio dataset all covers of a query track - has long been a challenging theoretical problem in MIR community. It also became a practical need for music composers societies requiring to detect automatically if an audio excerpt embeds musical conte…
Music listening context such as location or activity has been shown to greatly influence the users' musical tastes. In this work, we study the relationship between user context and audio content in order to enable context-aware music recommendation agnostic to user data. For that, we propose a semi-…
Most music streaming services rely on automatic recommendation algorithms to exploit their large music catalogs. These algorithms aim at retrieving a ranked list of music tracks based on their similarity with a target music track. In this work, we propose a method for direct recommendation based on…
This paper revisits the Degenerate Unmixing Estimation Technique (duet) for blind audio separation of an arbitrary number of sources given two mixtures through a recursively computed and adaptive time-frequency representation. Recently, synchrosqueezing was introduced as a promising signal disentang…
In this paper, we propose a set of audio features to describe the quality of an audio signal. Audio quality is here considered as being modified by the chain of processes/effects applied to the individual instrument tracks to obtain the final mix of a musical piece. Thus, the quality also depends on…