Signalfynd

Signalfynd is a modular software framework for real-time signal analysis and anomaly detection. It provides a pluggable architecture that enables constructing data processing pipelines from reusable blocks such as filters, transforms, feature extractors, and detectors. The framework is designed to handle multiple data streams with low latency and to run on edge devices or in the cloud.

Architectural overview: A core runtime coordinates pipelines described in a YAML-based domain-specific language or through a programmatic API. Data sources publish streams to a messaging layer; processing blocks consume inputs, produce outputs, and route results to sinks or alert systems. Signalfynd supports time-series data, event streams, and batched data. It includes GPU-accelerated kernels for compute-intensive tasks and a plugin system for sensors, codecs, and ML models.

Algorithms and features: The platform ships with built-in anomaly detectors ranging from statistical methods (z-score, EWMA) to machine learning models (autoencoders, isolation forests). It exposes a model-agnostic interface so users can integrate third-party algorithms. Visualization tools enable inspection of waveforms, spectrograms, and anomaly timelines. It supports streaming dashboards, persistence, and reproducible pipelines.

Applications and adoption: Signalfynd is used in industrial monitoring, energy management, manufacturing, telecommunications, and academic research. Deployments emphasize real-time alerts, retrospective analyses, and data exploration. It is distributed under an open-source license with a growing ecosystem of plugins and drivers.

History and reception: Signalfynd originated as an open-source project to unify DSP and ML-based anomaly detection. It has been praised for modularity and cross-platform support, though some users report a learning curve and tuning requirements in constrained environments.