Paper Archive

Information about the human spoken and singing voice is conveyed through the articulations of the individual’s vocal folds and vocal tract. The signal receiver, either human or machine, works at different levels of abstraction to extract and interpret only the relevant context specific information needed. Traditionally in the field of human machine interaction, the human voice is used to drive and control events that are discrete in terms of time and value. We propose to use the voice as a source of realvalued and time-continuous control signals that can be employed to interact with any multidimensional human-controllable device in real-time. The isolation of noise sources and the independence of the control dimensions play a central role. Their dependency on individual voice represents an additional challenge. In this paper we introduce a method to compute case specific independent signals from the vocal sound, together with an individual study of features computation and selection for noise rejection.

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We present Clean2FX, a study and demo of label-conditioned clean-to-effect transformation for electric guitar audio. Given a clean guitar input and a target effect label, the task is to synthesize the corresponding effected signal while preserving the musical content. Training and evaluation pairs are constructed from EGFxSet real, single-tone recordings by assembling matched clean/effected chords, melodies, and mixed timelines. This allows for controlled comparison across effects. We evaluate four neural approaches under a common spectrogram-based transformation setting: two variational autoencoders and two U-Net models that differ in whether they operate on linear or log-magnitude representations. Performance is measured using linear-magnitude spectrogram MSE and Fréchet Audio Distance. The U-Net models outperform the variational autoencoder variants. Per-effect results show that distortion effects are most readily improved, whereas delay and reverb effects exhibit weaker FAD gains despite substantial spectral-error reductions. A conditioning-sensitivity diagnostic provides evidence that the best model responds to target labels rather than collapsing to a single transformation. Our demo website compares two models applied on real-world guitar performances outside training and validation data, providing audio and spectrogram examples of the practical clean-to-effect behavior.

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A method for Binaural In-Ear Monitoring (Binaural IEM) of acoustic instruments in live music is presented. Spatial rendering is based on four considerations: the directional radiation patterns of musical instruments, room acoustics, binaural synthesis with Head-Related Transfer Functions (HRTF), and the movements of both the musician’s head and instrument. The concepts of static and dynamic sound mixes are presented and discussed according to the emotional involvement and musical instruments of the performers, as well as the use of motion capture technology. Pilot experiments of BIEM with dynamic mixing were done with amateur musicians performing with wireless headphones and a motion capture system in a small room. Listening tests with professional musicians evaluating recordings under conditions of dynamic sound mixing were carried out, attempting to find an initial reaction to BIEM. Ideas for further research in static sound mixing, individualized HRTFs, tracking techniques, as well as wedge-monitoring schemes are suggested.

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Spatial auditory acuity is contingent on the quality of spatial cues presented during listening. Electroencephalography (EEG) shows promise for finding neural markers of such acuity present in recorded neural activity, potentially mitigating common challenges with behavioural assessment (e.g., sound source localisation tasks). This study presents findings from three preliminary experiments which investigated neural response variations to auditory stimuli under different spatial listening conditions: free-field (loudspeakerbased), individual Head-Related Transfer-Functions (HRTF), and non-individual HRTFs. Three participants, each participating in one experiment, were exposed to auditory stimuli from various spatial locations while neural activity was recorded via EEG. The resultant neural responses underwent a decoding protocol to asses how decoding accuracy varied between stimuli locations over time. Decoding accuracy was highest for free-field auditory stimuli, with significant but lower decoding accuracy between left and right hemisphere locations for individual and non-individual HRTF stimuli. A latency in significant decoding accuracy was observed between listening conditions for locations dominated by spectral cues. Furthermore, findings suggest that decoding accuracy between free-field and non-individual HRTF stimuli may reflect behavioural front-back confusion rates.

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In this paper we present graphical tools and parameters search algorithms for the timbre space exploration and design of complex sounds generated by physical modeling synthesis. The tools are built around a sparse representation of sounds based on Gammatone functions and provide the designer with both a graphical and an auditory insight. The auditory representation of a number of reference sounds, located as landmarks in a 2D sound design space, provides the designer with an effective aid to direct his search for new sounds. The sonic landmarks can either be synthetic sounds chosen by the user or be automatically derived by using clever parameter search and clustering algorithms. The proposed probabilistic method in this paper makes use of the sparse representations to model the distance between sparsely represented sounds. A subsequent optimization model minimizes those distances to estimate the optimal parameters, which generate the landmark sounds on the given auditory landscape.

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In this paper, we study tempo estimation using spectral templates coming from unsupervised or supervised learning given a database annotated into tempo. More precisely, we study the inclusion of these templates in our tempo estimation algorithm of [1]. For this, we consider as periodicity observation a 48-dimensions vector obtained by sampling the value of the amplitude of the DFT at tempo-related frequencies. We name it spectral template. A set of reference spectral templates is then learned in an unsupervised or supervised way from an annotated database. These reference spectral templates combined with all the possible tempo assumptions constitute the hidden states which we decode using a Viterbi algorithm. Experiments are then performed on the “ballroom dancer” test-set which allows concluding on improvement over state-ofthe-art. In particular, we discuss the use of prior tempo probabilities. It should be noted however that these results are only indicative considering that the training and test-set are the same in this preliminary experiment.

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This work presents sfFDN, a modular and real-time-capable C++ library for Feedback Delay Networks (FDNs), together with the companion FDN Sandbox application designed for interactive experimentation, analysis, and parameter optimization. The library implements the canonical FDN as well as several recent extensions, including filter feedback matrices, velvet-noise decorrelation filters, and two-stage graphic equalizers for attenuation and tone correction. The Sandbox application exposes these features through a graphical interface, providing a suite of real-time visualizations, as well as an optimization framework supporting nine algorithms from the ensmallen library, with built-in loss functions for both colorless reverberation and room impulse response matching. Both the library and the application are open-source.

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Variational autoencoders (VAEs) are powerful (deep) generative artificial neural networks. They have been recently used in several papers for speech and audio processing, in particular for the modeling of speech/audio spectrograms. In these papers, very poor theoretical support is given to justify the chosen data representation and decoder likelihood function or the corresponding cost function used for training the VAE. Yet, a nice theoretical statistical framework exists and has been extensively presented and discussed in papers dealing with nonnegative matrix factorization (NMF) of audio spectrograms and its application to audio source separation. In the present paper, we show how this statistical framework applies to VAE-based speech/audio spectrogram modeling. This provides the latter insights on the choice and interpretability of data representation and model parameterization.

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In this paper, we address automating and systematizing the process of finding black-box models for virtual analogue audio effects with an optimal balance between error and latency. We introduce a multi-objective optimization approach based on hardware-aware neural architecture search which allows specifying the optimization balance of model error and latency according to the requirements of the application. By using a regularized evolutionary algorithm, it is able to navigate through a huge search space systematically. Additionally, we propose a search space for modelling non-linear dynamic audio effects consisting of over 41 trillion different WaveNet-style architectures. We evaluate its performance and usefulness by yielding highly effective architectures, either up to 18× faster or with a test loss of up to 56% less than the best performing models of the related work, while still showing a favourable trade-off. We can conclude that hardware-aware neural architecture search is a valuable tool that can help researchers and engineers developing virtual analogue models by automating the architecture design and saving time by avoiding manual search and evaluation through trial-and-error.

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This paper addresses identification of nonlinear circuits for power-balanced virtual analog modeling and simulation. The proposed method combines a port-Hamiltonian system formulation with kernel-based methods to retrieve model laws from measurements. This combination allows for the estimated model to retain physical properties that are crucial for the accuracy of simulations, while representing a variety of nonlinear behaviors. As an illustration, the method is used to identify a nonlinear passive peaking EQ.

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