19 accepted papers
The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present *context length probing*, a novel explanation technique for causal language models, based on tracking the predictions of a model as a function of the length of availa…
Recent advances in Transformer models allow for unprecedented sequence lengths, due to linear space and time complexity. In the meantime, relative positional encoding (RPE) was proposed as beneficial for classical Transformers and consists in exploiting lags instead of absolute positions for inferen…
By building upon the recent theory that established the connection between implicit generative modeling (IGM) and optimal transport, in this study, we propose a novel parameter-free algorithm for learning the underlying distributions of complicated datasets and sampling from them. The proposed algor…
This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders. The noise model remains unsupervised because we do not assume prior knowledge of the noisy recording environment. In t…
In this study, we propose a novel probabilistic model for separating clean speech signals from noisy mixtures by decomposing the mixture spectra into a structured speech part and a more flexible residual part. The main novelty in our model is that it uses a family of heavy-tailed distributions, so c…
Audio source separation comes with the need to devise multichannel filters that can exploit priors about the target signals. In that context, experience shows that modeling magnitude spectra is effective. However, devising a probabilistic model on complex spectral data with a prior on magnitudes is…
We propose a new blind source separation algorithm based on mixtures of α-stable distributions. Complex symmetric α-stable distributions have been recently showed to better model audio signals in the time-frequency domain than classical Gaussian distributions thanks to their larger dynamic range. Ho…
Live concert recordings consist in long multitrack audio samples with significant interferences between channels. For audio engineering purposes, it is desirable to attenuate those interferences. Recently, we proposed an algorithm to this end based on Non-negative Matrix Factorization, that iterativ…
We propose the Multi-resolution Common Fate Transform (MCFT), a signal representation that increases the separability of audio sources with significant energy overlap in the time-frequency domain. The MCFT combines the desirable features of two existing representations: the invertibility of the rece…
In this paper, we focus on modeling multichannel audio signals in the short-time Fourier transform domain for the purpose of source separation. We propose a probabilistic model based on a class of heavy-tailed distributions, in which the observed mixtures and the latent sources are jointly modeled b…
Upmixing consists in extracting audio objects out of their downmix, given some parameters computed beforehand at a coding stage. It is an important task in audio processing with many applications in the entertainment industry. One particularly successful approach for this purpose is to compress the…
In this paper, we propose a simple user-assisted method for the recovery of repeating patterns in time and frequency which can occur in audio mixtures. Here, the user selects a region in a log-frequency spectrogram from which they seek to recover the underlying pattern, such as a repeating chord mas…
In this paper, we show that tensor compression techniques based on randomization and partial observations are very useful for spatial audio object coding. In this application, we aim at transmitting several audio signals called objects from a coder to a decoder. A common strategy is to transmit only…
In this paper we present a novel source separation method aiming to overcome the difficulty of modelling non-stationary signals. The method can be applied to mixtures of musical instruments with frequency and/or amplitude modulation, e.g. typically caused by vibrato. It is based on a signal represen…
We propose a projection-based method for the unmixing of multichannel audio signals into their different constituent spatial objects. Here, spatial objects are modelled using a unified framework which handles both point sources and diffuse sources. We then propose a novel methodology to estimate and…
Repetition is a fundamental element in generating and perceiving structure in audio. Especially in music, structures tend to be composed of patterns that repeat through time (e.g., rhythmic elements in a musical accompaniment), and also frequency (e.g., different notes of the same instrument). The a…
In the recent years, many studies have focused on the single-sensor separation of independent waveforms using so-called soft-masking strategies, where the short term Fourier transform of the mixture is multiplied element-wise by a ratio of spectrogram models. When the signals are wide-sense stationa…
When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In…
Recently, Kernel Additive Modelling (KAM) was proposed as a unified framework to achieve multichannel audio source separation. Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability ove…