Download Pywdf: An Open Source Library for Prototyping and Simulating Wave Digital Filter Circuits in Python This paper introduces a new open-source Python library for the modeling and simulation of wave digital filter (WDF) circuits. The library, called pwydf, allows users to easily create and analyze WDF circuit models in a high-level, object-oriented manner. The library includes a variety of built-in components, such as voltage sources, capacitors, diodes etc., as well as the ability to create custom components and circuits. Additionally, pywdf includes a variety of analysis tools, such as frequency response and transient analysis, to aid in the design and optimization of WDF circuits. We demonstrate the library’s efficacy in replicating the nonlinear behavior of an analog diode clipper circuit, and in creating an allpass filter that cannot be realized in the analog world. The library is well-documented and includes several examples to help users get started. Overall, pywdf is a powerful tool for anyone working with WDF circuits, and we hope it can be of great use to researchers and engineers in the field.
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 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 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 Probabilistic Reverberation Model Based on Echo Density and Kurtosis This article proposes a probabilistic model for synthesizing room impulse responses (RIRs) for use in convolution artificial reverberators. The proposed method is based on the concept of echo density. Echo density is a measure of the number of echoes per second in an impulse response and is a demonstrated perceptual metric of artificial reverberation quality. As echo density is related to the statistical measure of kurtosis, this article demonstrates that the statistics of an RIR can be modeled using a probabilistic mixture model. A mixture model designed specifically for modeling RIRs is proposed. The proposed method is useful for statistically replicating RIRs of a measured environment, thereby synthesizing new independent observations of an acoustic space. A perceptual pilot study is carried out to evaluate the fidelity of the replication process in monophonic and stereo artificial reverberators.
Download Neural Grey-Box Guitar Amplifier Modelling with Limited Data This paper combines recurrent neural networks (RNNs) with the discretised Kirchhoff nodal analysis (DK-method) to create a grey-box guitar amplifier model. Both the objective and subjective results suggest that the proposed model is able to outperform a baseline black-box RNN model in the task of modelling a guitar amplifier, including realistically recreating the behaviour of the amplifier equaliser circuit, whilst requiring significantly less training data. Furthermore, we adapt the linear part of the DK-method in a deep learning scenario to derive multiple state-space filters simultaneously. We frequency sample the filter transfer functions in parallel and perform frequency domain filtering to considerably reduce the required training times compared to recursive state-space filtering. This study shows that it is a powerful idea to separately model the linear and nonlinear parts of a guitar amplifier using supervised learning.
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 Feature Based Delay Line Using Real-Time Concatenative Synthesis In this paper we introduce a novel approach utilizing real-time concatenative synthesis to produce a Feature-Based Delay Line (FBDL). Expanding upon the concept of a traditional delay, its most basic function is familiar – a dry signal is copied to an audio buffer whose read position is time shifted producing a delayed or "wet" signal that is then remixed with the dry. In our implementation, however, the traditionally unaltered wet signal is modified such that the audio delay buffer is segmented and concatenated according to specific audio features. Specifically, the input audio is analyzed and segmented as it is written to the delay buffer, where delayed segments are matched to a target feature set, such that the most similar segments are selected to constitute the wet signal of the delay. Targeting methods, either manual or automated, can be used to explore the feature space of the delay line buffer based on dry signal feature information and relevant targeting parameters, such as delay time. This paper will outline our process, detailing important requirements such as targeting and considerations for feature extraction and concatenation synthesis, as well as discussing use cases, performance evaluation, and commentary on the potential of advances to digital delay lines.
Download Upcylcing Android Phones into Embedded Audio Platforms There are millions of sophisticated Android phones in the world that get disposed of at a very high rate due to consumerism. Their computational power and built-in features, instead of being wasted when discarded, could be repurposed for creative applications such as musical instruments and interactive audio installations. However, audio programming on Android is complicated and comes with restrictions that heavily impact performance. To address this issue, we present LDSP, an open-source environment that can be used to easily upcycle Android phones into embedded platforms optimized for audio synthesis and processing. We conducted a benchmark study to compare the number of oscillators that can be run in parallel on LDSP with an equivalent audio app designed according to modern Android standards. Our study tested six phones ranging from 2014 to 2018 and running different Android versions. The results consistently demonstrate that LDSP provides a significant boost in performance, with some cases showing an increase of more than double, making even very old phones suitable for fairly advanced audio applications.