Computing ecosystems: neural networks and embedded hardware platforms

Presented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsPresented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsPresented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsPresented at the CHI2023 Workshop [WS2] - Beyond Prototyping Boards: Future Paradigms for Electronics ToolkitsEmbedded hardware platforms such as single-board computers (e.g., Raspberry Pi, Bela) or microcontrollers (e.g., Teensy, Arduino Uno) offer an entry point for beginners into physical computing. However, deploying neural networks into these platforms is challenging for various reasons: It requires lower-level software development skills, as machine learning toolkits are typically not incorporated into these platforms. Besides, the long compilation times burden debugging and quick prototyping and experimentation. Due to the low-resource nature of embedded hardware platforms, neural networks are usually trained on a host machine, which involves a back-and-forth of data, platforms and programming languages. We inquire how these computing ecosystems might be designed to facilitate prototyping and experimentation and integrate into existing programming workflows

Differentiable Modelling of Percussive Audio with Transient and Spectral Synthesis

Differentiable digital signal processing (DDSP) techniques, including methods for audio synthesis, have gained attention in recent years and lend themselves to interpretability in the parameter space. However, current differentiable synthesis methods have not explicitly sought to model the transient portion of signals, which is important for percussive sounds. In this work, we present a unified synthesis framework aiming to address transient generation and percussive synthesis within a DDSP framework. To this end, we propose a model for percussive synthesis that builds on sinusoidal modeling synthesis and incorporates a modulated temporal convolutional network for transient generation. We use a modified sinusoidal peak picking algorithm to generate time-varying non-harmonic sinusoids and pair it with differentiable noise and transient encoders that are jointly trained to reconstruct drumset sounds. We compute a set of reconstruction metrics using a large dataset of acoustic and electronic percussion samples that show that our method leads to improved onset signal reconstruction for membranophone percussion instruments

C-Ring Strength of Advanced Monolithic Ceramics

Alumina, silicon carbide, silicon nitride, and zirconia are common candidate ceramics for load-bearing tubular components. To help facilitate design and reliability modeling with each ceramic, Weibull strength distributions were determined with each material using a diametrally compressed c-ring specimen in accordance with ASTM C1323. The investigated silicon nitride and zirconia were found to exhibit higher uncensored characteristic strengths than the alumina and silicon carbide. The occurrence of chamfer-located fracture initiation was problematic, and hindered the ability to generate valid design data in some of these ceramics. Fractography and stress modeling results suggest that some aspects of ASTM C1323 should be revised to further minimize the frequency of chamfer-located failure initiation in c-ring test specimens

DDX7: Differentiable FM Synthesis of Musical Instrument Sounds

FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by Deep Neural Networks (DNNs) that learn to control differentiable synthesis layers from arbitrary sound inputs. The training process involves a corpus of audio for supervision, and spectral reconstruction loss functions. Such functions, while being great to match spectral amplitudes, present a lack of pitch direction which can hinder the joint optimization of the parameters of FM synthesizers. In this paper, we take steps towards enabling continuous control of a well-established FM synthesis architecture from an audio input. Firstly, we discuss a set of design constraints that ease spectral optimization of a differentiable FM synthesizer via a standard reconstruction loss. Next, we present Differentiable DX7 (DDX7), a lightweight architecture for neural FM resynthesis of musical instrument sounds in terms of a compact set of parameters. We train the model on instrument samples extracted from the URMP dataset, and quantitatively demonstrate its comparable audio quality against selected benchmarks

DMRN+16: Digital Music Research Network One-day Workshop 2021

DMRN+16: Digital Music Research Network One-day Workshop 2021 Queen Mary University of London Tuesday 21st December 2021 Keynote speakers Keynote 1. Prof. Sophie Scott -Director, Institute of Cognitive Neuroscience, UCL. Title: "Sound on the brain - insights from functional neuroimaging and neuroanatomy" Abstract In this talk I will use functional imaging and models of primate neuroanatomy to explore how sound is processed in the human brain. I will demonstrate that sound is represented cortically in different parallel streams. I will expand this to show how this can impact on the concept of auditory perception, which arguably incorporates multiple kinds of distinct perceptual processes. I will address the roles that subcortical processes play in this, and also the contributions from hemispheric asymmetries. Keynote 2: Prof. Gus Xia - Assistant Professor at NYU Shanghai Title: "Learning interpretable music representations: from human stupidity to artificial intelligence" Abstract Gus has been leading the Music X Lab in developing intelligent systems that help people better compose and learn music. In this talk, he will show us the importance of music representation for both humans and machines, and how to learn better music representations via the design of inductive bias. Once we got interpretable music representations, the potential applications are limitless

FM Tone Transfer with Envelope Learning

Tone Transfer is a novel deep-learning technique for interfacing a sound source with a synthesizer, transforming the timbre of audio excerpts while keeping their musical form content. Due to its good audio quality results and continuous controllability, it has been recently applied in several audio processing tools. Nevertheless, it still presents several shortcomings related to poor sound diversity, and limited transient and dynamic rendering, which we believe hinder its possibilities of articulation and phrasing in a real-time performance context. In this work, we present a discussion on current Tone Transfer architectures for the task of controlling synthetic audio with musical instruments and discuss their challenges in allowing expressive performances. Next, we introduce Envelope Learning, a novel method for designing Tone Transfer architectures that map musical events using a training objective at the synthesis parameter level. Our technique can render note beginnings and endings accurately and for a variety of sounds; these are essential steps for improving musical articulation, phrasing, and sound diversity with Tone Transfer. Finally, we implement a VST plugin for real-time live use and discuss possibilities for improvement

Leptospirosis: una enfermedad latente

En medicina veterinaria se entiende como enfermedad reproductiva, aquella que imposibilita o dificulta la fecundación, el mantenimiento de una gestación completa o la obtención de una cría con posibilidades de vida, o bien aquella enfermedad que afecta los parámetros reproductivos propios del sistema de producción que se maneje (Anderson, 2007). Durante el ciclo reproductivo del bovino se pueden presentar diversas pérdidas prenatales y posnatales: en el servicio, en la concepción, durante el período embrionario, fetal y neonatal (Morrel, 2010).Aunque nuestro país sufre importantes pérdidas por enfermedades que afectan la reproducción de los bovinos,sólo se conocen el 33% de las causas abortigénicas (Mooreet al., 2001). El objetivo del presente trabajo fue determinar asociaciones entre precipitaciones y presentación de casos positivos a Leptospira interrogans en cualquiera de sus serovares presentes en la provincia de Corrientes-Argentina. La presentación de mayores precipitaciones facilitaría la diseminación de la enfermedad, debido a que la bacteria no sobrevive mucho tiempo en el medio ambiente sin las condiciones adecuadas siendo sensible a la desecación. Sin embargo, según nuestros resultados, no hay evidencias deque este factor por sí solo, pueda ser determinante para la transmisión de la enfermedad bajo nuestras condiciones.Fil: Della Rosa, Paola. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; ArgentinaFil: Berecochea, F.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; ArgentinaFil: Sala, Juan M.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; ArgentinaFil: Morel, Victoria Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; ArgentinaFil: Biotti, Graciela. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; ArgentinaFil: Bevans, Walter. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; ArgentinaFil: Gómez, Sebastián. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; ArgentinaFil: Caspe, Sergio Gastón. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Mercedes; Argentin