Download End-to-end equalization with convolutional neural networks This work aims to implement a novel deep learning architecture to perform audio processing in the context of matched equalization. Most existing methods for automatic and matched equalization show effective performance and their goal is to find a respective transfer function given a frequency response. Nevertheless, these procedures require a prior knowledge of the type of filters to be modeled. In addition, fixed filter bank architectures are required in automatic mixing contexts. Based on end-to-end convolutional neural networks, we introduce a general purpose architecture for equalization matching. Thus, by using an end-toend learning approach, the model approximates the equalization target as a content-based transformation without directly finding the transfer function. The network learns how to process the audio directly in order to match the equalized target audio. We train the network through unsupervised and supervised learning procedures. We analyze what the model is actually learning and how the given task is accomplished. We show the model performing matched equalization for shelving, peaking, lowpass and highpass IIR and FIR equalizers.
Download Optimization techniques for a physical model of human vocalisation We present a non-supervised approach to optimize and evaluate the synthesis of non-speech audio effects from a speech production model. We use the Pink Trombone synthesizer as a case study of a simplified production model of the vocal tract to target nonspeech human audio signals –yawnings. We selected and optimized the control parameters of the synthesizer to minimize the difference between real and generated audio. We validated the most common optimization techniques reported in the literature and a specifically designed neural network. We evaluated several popular quality metrics as error functions. These include both objective quality metrics and subjective-equivalent metrics. We compared the results in terms of total error and computational demand. Results show that genetic and swarm optimizers outperform least squares algorithms at the cost of executing slower and that specific combinations of optimizers and audio representations offer significantly different results. The proposed methodology could be used in benchmarking other physical models and audio types.
Download Physically Derived Synthesis Model of a Cavity Tone The cavity tone is the sound generated when air flows over the open surface of a cavity and a number of physical conditions are met. Equations obtained from fluid dynamics and aerodynamics research are utilised to produce authentic cavity tones without the need to solve complex computations. Synthesis is performed with a physical model where the geometry of the cavity is used in the sound synthesis calculations. The model operates in real-time making it ideal for integration within a game or virtual reality environment. Evaluation is carried out by comparing the output of our model to previously published experimental, theoretical and computational results. Results show an accurate implementation of theoretical acoustic intensity and sound propagation equations as well as very good frequency predictions. NOMENCLATURE c = speed of sound (m/s) f = frequency (Hz) ω = angular frequency = 2πf (rads/revolution) u = air flow speed (m/s) Re = Reynolds number (dimensionless) St = Strouhal number (dimensionless) r = distance between listener and sound source (m) φ = elevation angle between listener and sound source ϕ = azimuth angle between listener and sound source ρair = mass density of air (kgm−3 ) µair = dynamic viscosity of air (Pa s) M = Mach number, M = u/c (dimensionless) L = length of cavity (m) d = depth of cavity (m) b = width of cavity (m) κ = wave number, κ = ω/c (dimensionless) r = distance between source and listener (m) δ = shear layer thickness (m) δ ∗ = effective shear layer thickness (m) δ0 = shear layer thickness at edge separation (m) θ0 = shear layer momentum thickness at edge separation (m) C2 = pressure coefficient (dimensionless)
Download Vocal Tract Area Estimation by Gradient Descent Articulatory features can provide interpretable and flexible controls for the synthesis of human vocalizations by allowing the user to directly modify parameters like vocal strain or lip position. To make this manipulation through resynthesis possible, we need to estimate the features that result in a desired vocalization directly from audio recordings. In this work, we propose a white-box optimization technique for estimating glottal source parameters and vocal tract shapes from audio recordings of human vowels. The approach is based on inverse filtering and optimizing the frequency response of a waveguide model of the vocal tract with gradient descent, propagating error gradients through the mapping of articulatory features to the vocal tract area function. We apply this method to the task of matching the sound of the Pink Trombone, an interactive articulatory synthesizer, to a given vocalization. We find that our method accurately recovers control functions for audio generated by the Pink Trombone itself. We then compare our technique against evolutionary optimization algorithms and a neural network trained to predict control parameters from audio. A subjective evaluation finds that our approach outperforms these black-box optimization baselines on the task of reproducing human vocalizations.
Download Modelling Experts’ Decisions on Assigning Narrative Importances of Objects in a Radio Drama Mix There is an increasing number of consumers of broadcast audio who suffer from a degree of hearing impairment. One of the methods developed for tackling this issue consists of creating customizable object-based audio mixes where users can attenuate parts of the mix using a simple complexity parameter. The method relies on the mixing engineer classifying audio objects in the mix according to their narrative importance. This paper focuses on automating this process. Individual tracks are classified based on their music, speech, or sound effect content. Then the decisions for assigning narrative importance to each segment of a radio drama mix are modelled using mixture distributions. Finally, the learned decisions and resultant mixes are evaluated using the Short Term Objective Intelligibility, with reference to the narrative importance selections made by the original producer. This approach has applications for providing customizable mixes for legacy content, or automatically generated media content where the engineer is not able to intervene.
Download Modulation Extraction for LFO-driven Audio Effects Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic sweeping or widening effects. It has been shown that these effects can be modeled using neural networks when conditioned with the ground truth LFO signal. However, in most cases, the LFO signal is not accessible and measurement from the audio signal is nontrivial, hindering the modeling process. To address this, we propose a framework capable of extracting arbitrary LFO signals from processed audio across multiple digital audio effects, parameter settings, and instrument configurations. Since our system imposes no restrictions on the LFO signal shape, we demonstrate its ability to extract quasiperiodic, combined, and distorted modulation signals that are relevant to effect modeling. Furthermore, we show how coupling the extraction model with a simple processing network enables training of end-to-end black-box models of unseen analog or digital LFO-driven audio effects using only dry and wet audio pairs, overcoming the need to access the audio effect or internal LFO signal. We make our code available and provide the trained audio effect models in a real-time VST plugin1 .
Download Differentiable Attenuation Filters for Feedback Delay Networks We introduce a novel method for designing attenuation filters in
digital audio reverberation systems based on Feedback Delay Networks (FDNs). Our approach uses Second Order Sections (SOS)
of Infinite Impulse Response (IIR) filters arranged as parametric
equalizers (PEQ), enabling fine control over frequency-dependent
reverberation decay. Unlike traditional graphic equalizer designs,
which require numerous filters per delay line, we propose a scalable solution where the number of filters can be adjusted. The frequency, gain, and quality factor (Q) parameters are shared parameters across delay lines and only the gain is adjusted based on delay
length. This design not only reduces the number of optimization
parameters, but also remains fully differentiable and compatible
with gradient-based learning frameworks. Leveraging principles
of analog filter design, our method allows for efficient and accurate filter fitting using supervised learning. Our method delivers
a flexible and differentiable design, achieving state-of-the-art performance while significantly reducing computational cost.
Download Unsupervised Taxonomy of Sound Effects Sound effect libraries are commonly used by sound designers in a range of industries. Taxonomies exist for the classification of sounds into groups based on subjective similarity, sound source or common environmental context. However, these taxonomies are not standardised, and no taxonomy based purely on the sonic properties of audio exists. We present a method using feature selection, unsupervised learning and hierarchical clustering to develop an unsupervised taxonomy of sound effects based entirely on the sonic properties of the audio within a sound effect library. The unsupervised taxonomy is then related back to the perceived meaning of the relevant audio features.
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.