Skip to content


Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Syllable Detector

The syllable detector is a Mac app that uses CoreAudio to perform low-latency syllable detection based on a simple Matlab neural network trained using the training code created by @bwpearre. Audio sampling is highly tunable to tradeoff between detection latency/jitter and processing power. The app allows running multiple detectors through multiple audio devices and/or channels.


A binary version of the software can be downloaded from the releases tab.


Connection: The syllable detector software can process any standard audio input source. As a result, you can either connect a microphone or a line in to the standard ports on the computer, or you can use an external audio interface. If the input source supports multiple channels, instances of the detector can be specified independently for each channel.

To generate an output TTL signal, the application supports two options. An output signal can be sent via an audio channel through any valid audio interface (headphone jack or an external audio interface). The audio output must have at least the same number of channels as the input. Because of mixing and signal processing, the audio output signal can introduce an added delay of up to 5ms.

Alternatively, the output TTL signal can be sent via an Arduino pin. Load the MATLAB Arduino IO sketch onto the Arduino (the sketch is available in this repository, and enables controlling pins through a basic serial interface). Connect the Arduino to the computer via USB. Output TTL pulses for the first channel will be sent via pin 7, for the second channel via pin 8, etc.

Preparing the network: After using the training code to train a detector, use the convert_to_text.m file included in the repository to convert the detect to a text format that can be easily read by the Swift software.


  1. Launch the software.
  2. The first window will provide a network to select an input source (listing all audio sources available), as well as an output source (listing both audio outputs and any detected Arduino serial ports).
  3. Once you select both an input and output, a new window launches. From here, you can see all available input and output channels. Double click a channel to load a text version of a trained detector.
  4. Once configured, press run to begin monitoring the inputs.


Using Core Audio to perform STFT and pass the amplitude data into a Matlab trained neural network.







No packages published