Download Fourier Neural Operators for Sample-Rate-Independent Virtual Analog Modeling Neural networks that operate directly on time-domain signals are widely used for virtual analog (VA) modeling. A key limitation of these models is their dependence on the sampling rate used during training, which becomes implicitly encoded in the learned parameters, so that changing it generally alters the realized dynamics. Although architectural modifications to recurrent neural networks have been proposed to enable sample-rate independent operation, these approaches are inherently tailored to upsampling and do not accommodate downsampling scenarios. In this manuscript, we present a VA modeling framework based on Fourier Neural Operators (FNOs) adapted to process fixed-duration audio frames. The proposed formulation defines the learned mapping over a fixed temporal support and evaluates it on uniform grids of different densities, so that a model trained at a single sampling rate can be applied at unseen sampling resolutions. Numerical results on a nonlinear transistor circuit show that the proposed model achieves competitive accuracy in upsampling scenarios while remaining directly applicable to downsampling, unlike a sample-rate independent baseline recurrent architecture.
Download Categorisation of Distortion Profiles in Relation to Audio Quality Since digital audio is encoded as discrete samples of the audio waveform, much can be said about a recording by the statistical properties of these samples. In this paper, a dataset of CD audio samples is analysed; the probability mass function of each audio clip informs a feature set which describes attributes of the musical recording related to loudness, dynamics and distortion. This allows musical recordings to be classified according to their “distortion character”, a concept which describes the nature of amplitude distortion in mastered audio. A subjective test was designed in which such recordings were rated according to the perception of their audio quality. It is shown that participants can discern between three different distortion characters; ratings of audio quality were significantly different (F (1, 2) = 5.72, p < 0.001, η 2 = 0.008) as were the words used to describe the attributes on which quality was assessed (χ2 (8, N = 547) = 33.28, p < 0.001). This expands upon previous work showing links between the effects of dynamic range compression and audio quality in musical recordings, by highlighting perceptual differences.
Download Enhancing Automatic Chord Recognition via Pseudo-Labeling and Knowledge Distillation Automatic Chord Recognition (ACR) is constrained by the scarcity of aligned chord annotations, which are costly to acquire. At the same time, open-weight pre-trained models are more accessible than their proprietary training data. In this work, we present a two-stage training pipeline that leverages pre-trained models together with unlabeled audio. The proposed method decouples training into two stages. In the first stage, we use the pre-trained BTC model as a teacher to generate pseudo-labels for over 1,000 hours of diverse unlabeled audio and train a student model solely on these pseudo-labels. In the second stage, the student is continually trained on ground-truth labels as they become available. To prevent catastrophic forgetting of the representations learned in the first stage, we apply selective knowledge distillation (KD) from the teacher as a regularizer. In our experiments, two models (BTC, 2E1D) were used as students. In Stage 1, using only pseudo-labels, the BTC student achieves about 99% of the teacher's performance, while the 2E1D model achieves about 97% of the teacher's performance across seven standard mir_eval metrics. After continual training with labeled data in Stage 2, the resulting BTC student model consistently surpasses both the traditional supervised learning baseline and the original pre-trained teacher model across all metrics. The resulting 2E1D student model also outperforms the supervised baseline and approaches teacher-level performance, with both models demonstrating substantial gains on rare chord qualities.
Download Improving Spectral Analysis Precision with an Enhanced Phase Vocoder using Signal Derivatives The purpose of this presentation is to demonstrate the practical interest of an original improvement of the classic Fourier analysis. The n-th order short-time Fourier Transform (FTn) extends the classic short-time Fourier transform by also considering the first n signal derivatives. This technique greatly improves Fourier analysis precision not only in frequency and amplitude but also in time, thus minimizing the well-known problem of the trade-off of time versus frequency. The implementation of this analysis method leads to an enhanced phase vocoder particularly wellsuited for extracting spectral parameters from the sounds.
Download Audio style transfer with rhythmic constraints In this transformation we present a rhythmically constrained audio style transfer technique for automatic mixing and mashing of two audio inputs. In this transformation the rhythmic and timbral features of both input signals are combined together through the use of an audio style transfer process that transforms the files so that they adhere to a larger metrical structure of the chosen input. This is accomplished by finding beat boundaries of both inputs and performing the transformation on beat-length audio segments. In order for the system to perform a mashup between two signals, we reformulate the previously used audio style transfer loss terms into three loss functions and enable them to be independent of the input. We measure and compare rhythmic similarities of the transformed and input audio signals using their rhythmic envelopes to investigate the influence of the tested transformation objectives.
Download Adversarial Synthesis of Drum Sounds Recent advancements in generative audio synthesis have allowed for the development of creative tools for generation and
manipulation of audio. In this paper, a strategy is proposed for the
synthesis of drum sounds using generative adversarial networks
(GANs). The system is based on a conditional Wasserstein GAN,
which learns the underlying probability distribution of a dataset
compiled of labeled drum sounds. Labels are used to condition
the system on an integer value that can be used to generate audio
with the desired characteristics. Synthesis is controlled by an input
latent vector that enables continuous exploration and interpolation
of generated waveforms. Additionally we experiment with a training method that progressively learns to generate audio at different
temporal resolutions. We present our results and discuss the benefits of generating audio with GANs along with sound examples
and demonstrations.
Download Anti-Aliasing of Neural Distortion Effects via Model Fine Tuning Neural networks have become ubiquitous with guitar distortion
effects modelling in recent years. Despite their ability to yield
perceptually convincing models, they are susceptible to frequency
aliasing when driven by high frequency and high gain inputs.
Nonlinear activation functions create both the desired harmonic
distortion and unwanted aliasing distortion as the bandwidth of
the signal is expanded beyond the Nyquist frequency. Here, we
present a method for reducing aliasing in neural models via a
teacher-student fine tuning approach, where the teacher is a pretrained model with its weights frozen, and the student is a copy of
this with learnable parameters. The student is fine-tuned against
an aliasing-free dataset generated by passing sinusoids through
the original model and removing non-harmonic components from
the output spectra.
Our results show that this method significantly suppresses aliasing for both long-short-term-memory networks (LSTM) and temporal convolutional networks (TCN). In the
majority of our case studies, the reduction in aliasing was greater
than that achieved by two times oversampling. One side-effect
of the proposed method is that harmonic distortion components
are also affected.
This adverse effect was found to be modeldependent, with the LSTM models giving the best balance between
anti-aliasing and preserving the perceived similarity to an analog
reference device.
Download FM Synthesizer Audio-Parameter Shared Embeddings Given a target sound, finding the synthesizer preset that best reproduces it remains a core problem in sound design. Existing methods treat synthesis parameters as flat vectors, discarding the signal routing and parameter interactions that produce audio. We make two contributions. First, to learn a representation of parameters including their signal routing, we design a graph neural network whose message passing structure imitates FM signal processing. Second, we adapt the multimodal objective from SLAP to learn joint embeddings of audio and FM synthesizer parameters, enabling preset retrieval from a gallery. We focus on the Yamaha DX7, where six identical sinusoid operators interact according to one of 32 routing topologies. Our graph encoder's message passing weights are shared across all nodes and layers, enabling processing of arbitrary topologies of any size. When every topology is seen during training, the DX7-GNN and two baselines achieve strong audio-to-preset retrieval. When some topologies are held out for testing, the DX7-GNN substantially outperforms both baselines despite having the fewest parameters. Our ablations further support the claim that imitating FM signal flow in a parameter encoder improves generalization to unseen topologies.
Download Audio-to-Audio via Diffusion Warm Initialization In this paper, we propose diffusion warm initialization as a simple yet effective approach for a range of audio-to-audio transformation tasks. To illustrate the generality of the approach, we demonstrate its use in timbre transfer, MIDI-to-Real synthesis, and multiple audio enhancement tasks. We conduct a detailed empirical analysis on timbre transfer to investigate the role of the initialization time t_init. The effect of t_init is evaluated using pitch-based Jaccard Distance and Fréchet Audio Distance to quantify faithfulness to the input signal and alignment with the target distribution. Our results provide practical guidance for selecting t_init and show that, once properly chosen, a single pretrained diffusion model combined with warm initialization can support multiple transformation objectives without task-specific training or conditioning. Despite its simplicity, this approach already achieves competitive results when compared with more complex pipelines designed specifically for these tasks.
Download Interpretation and control in AM/FM-based audio effects This paper is a continuation of our first studies on AM/FM digital audio effects, where the AM/FM decomposition equations were reviewed and some exploratory examples of effects were introduced. In the current paper we present more insight on the signals obtained with the AM/FM decomposition, intending to illustrate manipulations in the AM/FM domain that can be applied as interesting audio effects. We provide high-quality AM/FM effects and their implementations, alongside a brief objective evaluation. Audio samples and codes for real-time operation are also supplied.