Download Parametric Spatial Audio Effects Based on the Multi-Directional Decomposition of Ambisonic Sound Scenes
Decomposing a sound-field into its individual components and respective parameters can represent a convenient first-step towards offering the user an intuitive means of controlling spatial audio effects and sound-field modification tools. The majority of such tools available today, however, are instead limited to linear combinations of signals or employ a basic single-source parametric model. Therefore, the purpose of this paper is to present a parametric framework, which seeks to overcome these limitations by first dividing the sound-field into its multi-source and ambient components based on estimated spatial parameters. It is then demonstrated that by manipulating the spatial parameters prior to reproducing the scene, a number of sound-field modification and spatial audio effects may be realised; including: directional warping, listener translation, sound source tracking, spatial editing workflows and spatial side-chaining. Many of the effects described have also been implemented as real-time audio plug-ins, in order to demonstrate how a user may interact with such tools in practice.
Download Making Sounds with Numbers: A Tutorial on Music Software Dedicated to Digital Audio
A (partial) taxonomy of software applications devoted to sounds is presented. For each category of software applications, an abstract model is proposed and actual implementations are evaluated with respect to this model.
Download Blind Upmix for Applause-like Signals Based on Perceptual Plausibility Criteria
Applause is the result of many individuals rhythmically clapping their hands. Applause recordings exhibit a certain temporal, timbral and spatial structure: claps originating from a distinct direction (i.e, from a particular person) usually have a similar timbre and occur in a quasi-periodic repetition. Traditional upmix approaches for blind mono-to-stereo upmix do not consider these properties and may therefore produce an output with suboptimal perceptual quality to be attributed to a lack of plausibility. In this paper, we propose a blind upmixing approach of applause-like signals which aims at preserving the natural structure of applause signals by incorporating periodicity and timbral similarity of claps into the upmix process and therefore supporting plausibility of the artificially generated spatial scene. The proposed upmix approach is evaluated by means of a subjective preference listening test.
Download Wave Pulse Phase Modulation: Hybridising Phase Modulation and Phase Distortion
This paper introduces Wave Pulse Phase Modulation (WPPM), a novel synthesis technique based on phase shaping. It combines two classic digital synthesis techniques: Phase Modulation (PM) and Phase Distortion (PD), aiming to overcome their respective limitations while enabling the creation of new, interesting timbres. It works by segmenting a phase signal into two regions, each independently driving the phase of a modulator waveform. This results in two distinct pulses per period that together form the signal used as the phase input to a carrier waveform, similar to PM, hence the name Wave Pulse Phase Modulation. This method provides a minimal set of parameters that enable the creation of complex, evolving waveforms, and rich dynamic textures. By modulating these parameters, WPPM can produce a wide range of interesting spectra, including those with formant-like resonant peaks. The paper examines PM and PD in detail, exploring the modifications needed to integrate them with WPPM, before presenting the full WPPM algorithm alongside its parameters and creative possibilities. Finally, it discusses scope for further research and developments into new similar phase shaping algorithms.
Download Musical Aspects of Vowel Formants in the Extreme Metal Voice
Download Equalizing Loudspeakers in Reverberant Environments Using Deep Convolutive Dereverberation
Loudspeaker equalization is an established topic in the literature, and currently many techniques are available to address most practical use cases. However, most of these rely on accurate measurements of the loudspeaker in an anechoic environment, which in some occurrences is not feasible. This is the case, e.g. of custom digital organs, which have a set of loudspeakers that are built into a large and geometrically-complex piece of furniture, which may be too heavy and large to be transported to a measurement room, or may require a big one, making traditional impulse response measurements impractical for most users. In this work we propose a method to find the inverse of the sound emission system in a reverberant environment, based on a Deep Learning dereverberation algorithm. The method is agnostic of the room characteristics and can be, thus, conducted in an automated fashion in any environment. A real use case is discussed and results are provided, showing the effectiveness of the approach in designing filters that match closely the magnitude response of the ideal inverting filters.
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.
Download Analytical Features for the Classification of Percussive Sounds: The Case of the Pandeiro
There is an increasing need for automatically classifying sounds for MIR and interactive music applications. In the context of supervised classification, we describe an approach that improves the performance of the general bag-of-frame scheme without loosing its generality. This method is based on the construction and exploitation of specific audio features, called analytical, as input to classifiers. These features are better, in a sense we define precisely than standard, general features, or even than ad hoc features designed by hand for specific problems. To construct these features, our method explores a very large space of functions, by composing basic operators in syntactically correct ways. These operators are taken from the Mathematical and Audio Processing domains. Our method allows us to build a large number of these features, evaluate and select them automatically for arbitrary audio classification problems. We present here a specific study concerning the analysis of Pandeiro (Brazilian tambourine) sounds. Two problems are considered: the classification of entire sounds, for MIR applications, and the classification of attacks portions of the sound only, for interactive music applications. We evaluate precisely the gain obtained by analytical features on these two problems, in comparison with standard approaches.
Download Balancing Error and Latency of Black-Box Models for Audio Effects Using Hardware-Aware Neural Architecture Search
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.
Download A Generative Model for Raw Audio Using Transformer Architectures
This paper proposes a novel way of doing audio synthesis at the waveform level using Transformer architectures. We propose a deep neural network for generating waveforms, similar to wavenet . This is fully probabilistic, auto-regressive, and causal, i.e. each sample generated depends on only the previously observed samples. Our approach outperforms a widely used wavenet architecture by up to 9% on a similar dataset for predicting the next step. Using the attention mechanism, we enable the architecture to learn which audio samples are important for the prediction of the future sample. We show how causal transformer generative models can be used for raw waveform synthesis. We also show that this performance can be improved by another 2% by conditioning samples over a wider context. The flexibility of the current model to synthesize audio from latent representations suggests a large number of potential applications. The novel approach of using generative transformer architectures for raw audio synthesis is, however, still far away from generating any meaningful music similar to wavenet, without using latent codes/meta-data to aid the generation process.