7 repositorios
Routing a signal through multiple filters or effects simultaneously and blending the outputs.
Distinct from Effects Processing: Focuses on parallel audio filtering rather than sequential effects processing or database filtering.
Explore 7 awesome GitHub repositories matching graphics & multimedia · Parallel Signal Processing. Refine with filters or upvote what's useful.
LMMS is a digital audio workstation and MIDI sequencer designed for composing, arranging, and mixing music. It functions as a comprehensive production environment that integrates a MIDI sequencer, a sample-based synthesizer, and an audio mixing console. The project distinguishes itself through a versatile synthesis engine that includes additive synthesis, wavetable generation, and emulations of vintage hardware such as NES audio and FM chips. It also serves as a VST plugin host, allowing for the integration of third-party virtual instruments and audio effects via a standardized interface. Be
Processes two different filters simultaneously on a single audio signal to blend their sonic characteristics.
Combines multiple effect chains in parallel and mixes their outputs for layered transformations.
BespokeSynth is a modular synthesizer environment that combines a node-based audio graph routing system with a live scripting bridge, enabling real-time patching and control without recompilation. Its architecture integrates a granular synthesis engine, FFT-based spectral processing, a MIDI Polyphonic Expression pipeline, and a Euclidean rhythm generator, all within a freeform workspace designed for live experimentation. The project distinguishes itself through its live coding capabilities, allowing Python scripts to create, modify, and control synthesis parameters at runtime, and its plugin
Implements a feedback loop module that feeds a delayed audio copy back into the processing chain.
Surge is a polyphonic software synthesizer and VST plugin designed for complex sound generation. It functions as a hybrid synthesis engine, combining subtractive, wavetable, frequency modulation, and physical modeling techniques to produce audio. The project distinguishes itself through extensive microtuning capabilities, supporting non-standard musical scales and intonations via industry-standard tuning files. It also features an Open Sound Control compatible architecture for remote parameter management and hardware synchronization. The system covers a broad range of audio engineering capab
Processes audio signals through various filter types with configurable routing and recursive feedback loops.
This project is a reference implementation and framework for building real-time communication systems. It provides an Android demo application for WebRTC video, audio, and data transmission, alongside a communication SDK for integrating instant messaging, VoIP, and multi-party video conferencing into mobile and web applications. The framework includes a P2P media streaming client for direct high-definition transmission between devices and an interactive whiteboard application that synchronizes drawing and screen sharing across connected users. It also provides a foundation for IM and VoIP sys
Filters audio input to remove feedback and noise for clear voice communication during real-time calls.
This project is a set of digital signal processing routines and firmware designed for building modular synthesis hardware and control voltage modules. It functions as a comprehensive system for Eurorack synthesis, incorporating a physical modeling synthesis engine, a granular synthesis module, and a dedicated control voltage processor. The system distinguishes itself through a specialized synthesis engine that simulates the acoustic properties of strings, reeds, and percussion using modal resonators. It also features a MIDI to CV converter for monophonic and polyphonic operation and a step se
Applies all-pass filters and blending effects including reverb and random panning to post-process granular signals.
Este proyecto es un toolkit integral para el reconocimiento de voz, síntesis y procesamiento de audio en el dispositivo, diseñado específicamente para Apple Silicon. Proporciona un framework para construir agentes de voz full-duplex en tiempo real que operan completamente offline, aprovechando la aceleración de hardware nativa para mantener el rendimiento y la privacidad. Al utilizar modelos de machine learning optimizados, la biblioteca permite la ejecución local de tareas de audio complejas sin depender de servicios externos en la nube. La biblioteca se distingue por su enfoque especializado en la interacción de voz local de alto rendimiento. Incluye una orquestación sofisticada para pipelines de audio en streaming, permitiendo la transcripción en tiempo real, síntesis de voz y clonación de voz con baja latencia. El sistema está diseñado para manejar conversaciones interactivas continuas, presentando mecanismos integrados para evitar bucles de retroalimentación de audio y gestionar sesiones de streaming persistentes. Más allá de la interacción central, el proyecto ofrece un amplio conjunto de capacidades de mejora y gestión de audio. Admite procesamiento de señales avanzado, incluyendo separación de fuentes, reducción de ruido y sobremuestreo de audio, junto con herramientas para diarización de hablantes y extracción de embeddings. El framework también proporciona amplias utilidades de gestión de modelos, como controles de cuantización, gestión de memoria y soporte para la carga de pesos de modelos personalizados, asegurando que los desarrolladores puedan equilibrar la velocidad de procesamiento y el consumo de recursos en hardware local. El proyecto incluye una interfaz de línea de comandos para ejecutar tareas de audio y convertir pesos de modelos en formatos optimizados. También expone endpoints HTTP y WebSocket para facilitar la integración con interfaces estándar de la industria.
Coordinates speech synthesis and recognition timing to prevent the system from capturing its own output as new input.