What Is Microphone Spectrum Analysis? Why Perform Spectrum Testing?
Microphone spectrum analysis, also known as audio spectrum testing or frequency response visualization, is a testing method that breaks down the sound captured by a microphone by frequency and displays the energy strength of each frequency band in real time. Unlike a microphone volume test that only shows the total volume, spectrum analysis can reveal the "frequency composition" of sound—how much low frequency there is, how much mid frequency there is, and whether high frequency is missing. It is also an important means of determining frequency response characteristics in microphone detection.
This tool is implemented entirely based on the browser's native Web Audio API and AnalyserNode interface, with no plug-ins or clients required. After clicking "Start Spectrum Analysis," the browser will request microphone permission. Once authorized, it will display the 20Hz–20kHz spectrum bar chart in real time and simultaneously calculate the energy proportions of five key frequency bands (low frequency, low-mid frequency, mid frequency, high-mid frequency, and high frequency). During testing, we actively disable echo cancellation, noise suppression, and automatic gain control (AGC) to ensure that the measured spectrum distribution is as close as possible to the true hardware performance.
How to Determine Whether a Microphone Is Normal Through the Spectrum?
During normal speech, human voice energy is mainly concentrated in the 100Hz–4kHz range, and the spectrum should show a "mountain peak" shape with high mid frequency and low ends. If there is almost no energy in the high-frequency band (above 6kHz) when you speak, it indicates missing high frequency in the microphone, which may be due to the microphone itself having a narrow frequency response, the sound pickup hole being blocked, or noise suppression enabled by the system cutting high frequencies as noise. Conversely, if the low-frequency band (below 250Hz) is abnormally prominent, it may be caused by the proximity effect or low-frequency muddiness from desktop vibration.
By observing the peak frequency and high-frequency energy ratio, you can quickly determine the sound pickup characteristics. A peak frequency between 200Hz–2kHz belongs to the normal human voice range; a high-frequency energy ratio between 5%–30% is relatively healthy. Below 5% indicates weak high frequency, while above 40% may indicate ambient noise or excessive sibilance. It is recommended to test at different distances and angles separately to find the microphone placement that suits you best. This is also a key step in testing whether the microphone frequency response is normal.
Application Scenarios of Microphone Spectrum Analysis
Microphone purchase and comparison. When you are hesitating among multiple microphones, spectrum analysis can provide a more intuitive basis for comparison than specification sheets. Test each microphone separately at the same distance and with the same speaking volume, and observe the differences in energy distribution in the high-frequency and low-frequency bands—those with richer high frequency are usually brighter, and those with fuller low frequency are usually thicker. Compared with the subjective judgment that "they sound about the same," the spectrum chart makes the differences visible to the naked eye.
Optimization for live streaming, podcasts, and recording. Before going live, record a segment of simulated speech with spectrum analysis to quickly determine whether the current microphone has problems such as missing high frequency, excessive low frequency, or harsh sibilance. If the high-frequency ratio remains below 5% for a long time, you can appropriately boost the high-frequency EQ in OBS or the sound card driver; if the low-frequency band is abnormally prominent, you can improve it through a high-pass filter or by adjusting the microphone distance.
Troubleshooting device and system issues. If the recorded playback always sounds muffled and details are hard to hear, spectrum analysis can help you confirm whether the problem lies in the microphone itself. For example, when the system sampling rate is set to 8kHz, the effective upper limit of the spectrum will drop significantly; when the system enables strong noise suppression, high-frequency band energy will be greatly reduced. With the spectrum chart, you can quickly distinguish whether it is a hardware frequency response problem or a software setting problem.
Audio teaching and principle demonstration. For users learning audio engineering or sound design, spectrum analysis is the most intuitive tool for understanding concepts such as FFT, frequency response, and harmonics. By speaking into the microphone, playing an instrument, or playing white noise, you can see the differences in frequency composition of different sounds in real time, which is easier to understand than pure theoretical learning. These scenarios all rely on the flexible use of microphone online testing.
Experience Sharing for Microphone Spectrum Analysis
First, testing with white noise or a sweep signal is more accurate. The high-frequency energy of the human voice itself is limited, and the spectrum shape is greatly affected by the speech content. If you want to test the complete frequency response range of the microphone, you can play a segment of white noise online or a sweep signal from 20Hz to 20kHz, so that the spectrum chart can more clearly show whether each frequency band is fully covered.
Second, wear headphones during testing to avoid feedback. If you play the test signal through speakers, the sound will be picked up again by the microphone, forming feedback or even howling, and abnormal spikes will appear on the spectrum chart. Wearing headphones can minimize this interference. Third, first observe the noise floor spectrum in a quiet environment. During the few seconds when you are not speaking, if there is always high low-frequency energy on the left side of the spectrum, it indicates that the ambient noise floor is relatively high (air conditioning, fans, desktop vibration, etc.), and you should stay away from these noise sources as much as possible during testing.
Fourth, test multiple times at different distances and angles. At 5 cm and 20 cm from the mouth, the spectrum shape will be noticeably different—the closer the distance, the more low frequency there is (proximity effect), and the farther the distance, the more obvious the high-frequency attenuation. It is recommended to test at commonly used distances to find the placement that best suits your usage scenario.
Fifth, combine with level testing for comprehensive judgment. Spectrum analysis only looks at frequency distribution, not total volume. If the spectrum shape is normal but the overall energy is very low, it means the gain is insufficient; if the spectrum shape is normal but the volume is constantly clipping, it means the gain is too high. It is recommended to use spectrum analysis together with a microphone volume test to comprehensively determine the microphone status.
Sixth, pay attention to the impact of the system sampling rate on the high-frequency upper limit. According to the Nyquist sampling theorem, the theoretical maximum frequency that can be captured at a 44.1kHz sampling rate is 22.05kHz, and at 48kHz it is 24kHz. If the system sampling rate is set to 16kHz or lower, there will be no energy above 8kHz in the spectrum, which is a system setting issue rather than a microphone failure. Mastering these techniques can make your microphone detection more efficient.
