Download Automatic Target Mixing using Least-Squares Optimization of Gains and Equalization Settings The proposed automatic target mixing algorithm determines the gains and the equalization settings for the mixing of a multi-track recording using a least-squares optimization. These parameters are estimated using a single channel target mix, that is a signal which contains the same audio tracks as the multi-track recording, but that has been previously mixed using some unknown settings. Several tests have been done in order to evaluate the performances of two different approaches to the optimization, namely the sub-band estimator and the FIR filters estimator. The results show that, using the latter technique, the proposed algorithm is able to retrieve the parameters originally applied to the target mix. This achievement can be useful for remastering applications, where both the original recording sessions and the final mix are available, but there is the need to retrieve the mixing parameters originally applied to the various audio tracks.
Download Informed Selection of Frames for Music Similarity Computation In this paper we present a new method to compute frame based audio similarities, based on nearest neighbour density estimation. We do not recommend it is as a practical method for large collections because of the high runtime. Rather, we use this new method for a detailed analysis to get a deeper insight on how a bag of frames approach (BOF) determines similarities among songs, and in particular, to identify those audio frames that make two songs similar from a machine’s point of view. Our analysis reveals that audio frames of very low energy, which are of course not the most salient with respect to human perception, have a surprisingly big influence on current similarity measures. Based on this observation we propose to remove these low-energy frames before computing song models and show, via classification experiments, that the proposed frame selection strategy improves the audio similarity measure.
Download Fully Conditioned and Low-Latency Black-Box Modeling of Analog Compression Neural networks have been found suitable for virtual analog modeling applications. Several analog audio effects have been successfully modeled with deep learning techniques, using low-latency and conditioned architectures suitable for real-world applications. Challenges remain with effects presenting more complex responses, such as nonlinear and time-varying input-output relationships. This paper proposes a deep-learning model for the analog compression effect. The architecture we introduce is fully conditioned by the device control parameters and it works on small audio segments, allowing low-latency real-time implementations. The architecture is used to model the CL 1B analog optical compressor, showing an overall high accuracy and ability to capture the different attack and release compression profiles. The proposed architecture’ ability to model audio compression behaviors is also verified using datasets from other compressors. Limitations remain with heavy compression scenarios determined by the conditioning parameters.
Download A Comparative Study of Kolmogorov-Arnold Networks and Multi-Layer Perceptrons for Virtual Analog Modeling in Wave Digital Filters The design of Virtual Analog (VA) algorithms has traditionally been divided between white-box (physics-based) and black-box (data-driven) approaches. Recent work has shown that hybrid methods, combining physical modeling with neural networks, can effectively leverage the strengths of both paradigms. In particular, Wave Digital Filters (WDFs) can be coupled with Multi-Layer Perceptrons (MLPs) to model circuits with multiple nonlinearities in a fully explicit manner. In this paper, we present a comparative study investigating the use of Kolmogorov-Arnold Networks (KANs) for VA modeling within the WDF framework. Unlike MLPs, KANs shift the learning paradigm by parameterizing activation functions instead of relying exclusively on learned weight matrices, potentially enabling more compact representations. Results show that, for our case study, KANs achieve accuracy comparable to MLPs while requiring approximately 70% fewer parameters at the cost of increased computational complexity. These findings suggest that KANs may represent a promising alternative in scenarios where memory footprint is a primary constraint, such as embedded audio applications, or when target models feature numerous nonlinear elements.
Download Designing a Library for Generative Audio in Unity This paper overviews URALi, a library designed to add generative sound synthesis capabilities to Unity. This project, in particular, is directed towards audiovisual artists keen on working with algorithmic systems in Unity but can not find native solutions for procedural sound synthesis to pair with their visual and control ones. After overviewing the options available in Unity concerning audio, this paper reports on the functioning and architecture of the library, which is an ongoing project.
Download Neural Morphing: Sequence-Optimized Token-Level Morphing in Neural Audio Codecs Neural audio codecs were originally developed for high-fidelity compression; however, their latent token representations and expressive decoders also constitute a powerful substrate for controllable audio transformation. This work introduces Neural Morphing, a training-free token-domain audio effect that selects residual-vector-quantized (RVQ) token grains from a user palette and decodes the edited stream through a pretrained codec. The method combines an RVQ-group transfer policy that separates coarse, middle, and fine codebook groups with a continuity-constrained sequence matcher that replaces independent greedy selection with bounded beam search. The intended output is a controlled hybrid: the source preserves rhythmic organization while the palette contributes timbral color and residual detail. We focus on the implementation and real-time behavior of a deployable VST3/AU system, including chunked rendering, palette-size scaling, and backend health checks.
Download Modeling the Impulse Response of Higher-Order Microphone Arrays Using Differentiable Feedback Delay Networks Recently, differentiable multiple-input multiple-output Feedback
Delay Networks (FDNs) have been proposed for modeling target multichannel room impulse responses by optimizing their parameters according to perceptually-driven time-domain descriptors. However, in spatial audio applications, frequency-domain
characteristics and inter-channel differences are crucial for accurately replicating a given soundfield. In this article, targeting the
modeling of the response of higher-order microphone arrays, we
improve on the methodology by optimizing the FDN parameters
using a novel spatially-informed loss function, demonstrating its
superior performance over previous approaches and paving the
way toward the use of differentiable FDNs in spatial audio applications such as soundfield reconstruction and rendering.
Download Neural Parametric Equalizer Matching Using Differentiable Biquads This paper proposes a neural network for carrying out parametric equalizer (EQ) matching. The novelty of this neural network
solution is that it can be optimized directly in the frequency domain by means of differentiable biquads, rather than relying solely
on a loss on parameter values which does not correlate directly
with the system output. We compare the performance of the proposed neural network approach with that of a baseline algorithm
based on a convex relaxation of the problem. It is observed that the
neural network can provide better matching than the baseline approach because it directly attempts to solve the non-convex problem. Moreover, we show that the same network trained with only
a parameter loss is insufficient for the task, despite the fact that it
matches underlying EQ parameters better than one trained with a
combination of spectral and parameter losses.
Download Semi-automatic Ambience Generation Ambiances are background recordings used in audiovisual productions to make listeners feel they are in places like a pub or a farm. Accessing to commercially available atmosphere libraries is a convenient alternative to sending teams to record ambiances yet they limit the creation in different ways. First, they are already mixed, which reduces the flexibility to add, remove individual sounds or change its panning. Secondly, the number of ambient libraries is limited. We propose a semi-automatic system for ambiance generation. The system creates ambiances on demand given text queries by fetching relevant sounds from a large sound effect database and importing them into a sequencer multitrack project. Ambiances of diverse nature can be created easily. Several controls are provided to the users to refine the type of samples and the sound arrangement.
Download The Mix Evaluation Dataset Research on perception of music production practices is mainly concerned with the emulation of sound engineering tasks through lab-based experiments and custom software, sometimes with unskilled subjects. This can improve the level of control, but the validity, transferability, and relevance of the results may suffer from this artificial context. This paper presents a dataset consisting of mixes gathered in a real-life, ecologically valid setting, and perceptual evaluation thereof, which can be used to expand knowledge on the mixing process. With 180 mixes including parameter settings, close to 5000 preference ratings and free-form descriptions, and a diverse range of contributors from five different countries, the data offers many opportunities for music production analysis, some of which are explored here. In particular, more experienced subjects were found to be more negative and more specific in their assessments of mixes, and to increasingly agree with each other.