6.0 Safe to install
Precision Detection at Your Fingertips: Low Frequency Detector Review

TOON, LLC's Low Frequency Detector is an intuitive tool designed for accurately identifying low-frequency sounds across various applications, making it a must-have for audiophiles and audio professionals alike.
Low Frequency Detector is a novel acoustic measurement tool designed to detect low-frequency sounds that are often challenging to perceive in normal environments.
Human hearing tends to be less sensitive to lower frequencies. Individuals may unknowingly be exposed to noise from nearby construction sites or factories, which can lead to discomfort and feelings of unease. This application offers a straightforward way to identify the specific type of low-frequency noise present.
In general, low-frequency sounds range from 1 Hz to 100 Hz. Depending on the frequency components and sound levels, the impact on human health and the perception of noise can vary significantly.
This app provides users with the ability to analyze the frequency components of low-frequency sounds through 1/3 octave band analysis and FFT analysis.
Features:
- Low-frequency power level measurement under 100Hz (LF Level): Shows a flat characteristic and updates measurements every 1 second.
- Sound power level measurement within the audible band (Leq): Flat characteristic with updates every 200ms.
- 1/3 octave band analysis (OCT)
- FFT analysis (FFT)
- Time course graph (TIME)
- Snapshots (Snap) can be taken on the measurement display screen.
- Report function (Report): Provides an analysis report of the peak LF Level with TIME, OCT, and FFT data.
- Measurement time: Up to 60 seconds.
Functions:
The measurement analysis occurs in the following steps. Ensure to set the measurement conditions in Step-1 before commencing sound measurement.
- Calibration (Calibrate): Outputs a reference signal (1 kHz sine wave) to establish the reference noise level for low-frequency components. The app automatically adjusts the volume level for optimal signal detection when the iPhone volume ranges between 20-50%.
- Measurement (Measure): Analyses the power level of low-frequency sound. - LF Level (dB): Low-frequency power level below 100Hz. - Leq (dB): Sound power level within the audible band with a flat frequency weight characteristic.
- Measurement display: Users can choose between 1/3 octave band frequency analysis, FFT analysis, or Time course graph. The displays are updated in real-time.
- Report: Generates a measurement result report, particularly effective for measurements lasting at least 10 seconds.
Notes:
- The app measures low-frequency sound levels relative to a reference signal (1 kHz sine wave), indicating relative power levels.
- Avoid adjusting the iPhone volume after calibration to maintain measurement accuracy.
- The white line in OCT and FFT modes represents frequency characteristics following G-weighting corrections.
- The built-in microphone of iPhones has reduced sensitivity to very low frequencies; avoid measuring near extremely loud noise sources.
- Expect some time for stabilization of observation values at the beginning of a measurement; maximum values are typically calculated around 2 seconds later.
Overview
Low Frequency Detector is a Commercial software in the category System Utilities developed by TOON,LLC.
The latest version of Low Frequency Detector is 6.0, released on 08/23/2024. It was initially added to our database on 08/23/2024.
Low Frequency Detector runs on the following operating systems: iOS.
Users of Low Frequency Detector gave it a rating of 3 out of 5 stars.

Peter Salakani
I'm Peter, a software reviews author at UpdateStar and content specialist with a keen focus on usability and performance. With a background in both software development and content creation, I bring a unique perspective to evaluating and discussing general software topics. When I'm not reviewing software, I enjoy staying updated on the latest tech trends, experimenting with new applications, and finding innovative solutions to everyday tech challenges.
Latest Reviews by Peter Salakani
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