The time-frequency uncertainty principle
Spectrograms are computed via the short-time Fourier transform (STFT): you slice the audio into short overlapping windows, compute the frequency content of each window, and stack the results into the 2D image. The window length is a parameter, and it forces a trade-off. Long windows give you fine frequency resolution but coarse time resolution — you can tell precisely what frequencies are present but you smear the exact moment they appear. Short windows give you fine time resolution but coarse frequency resolution — you can pinpoint when transients occur but the frequencies get blurry. This is the time-frequency uncertainty principle in signal processing, and it's a hard physical constraint, not a software limitation. Every you look at is showing you a specific time-frequency compromise; different software defaults produce different visualizations of the same underlying audio.
A spectrogram shows the magnitude of acoustic energy at each time-frequency point but not the phase.
Phase information is gone
A shows the magnitude of acoustic energy at each time-frequency point but not the phase. Phase carries information about waveform timing and shape — two sounds with identical magnitude spectrograms can sound noticeably different if their phase patterns differ. For most bioacoustic analysis, the magnitude information is enough; phase typically doesn't carry the species-discriminative or call-type-discriminative information of interest. But it's not zero. Specifically, fine-grained timbre information that distinguishes individuals of the same species can include phase-related features that get lost in spectrograms. AI models that operate on raw waveforms (rather than spectrograms) can potentially recover this information; most bioacoustic foundation models including BirdNET[1] operate on mel-scaled spectrograms and lose phase entirely.
Logarithmic versus linear frequency
Many spectrograms (especially bioacoustic ones) use mel-scaled or log-scaled frequency axes rather than linear. The mel scale compresses high frequencies and stretches low frequencies, matching human perceptual sensitivity. This is useful for some purposes (matching what human listeners notice) and misleading for others (visually overemphasizing low-frequency variation that may not matter biologically). When comparing spectrograms across papers or sources, the frequency-axis choice matters. A bird call at 4-5 kHz looks much closer to the middle of a mel-scaled than it does on a linear-frequency spectrogram of the same recording.
Color scheme is information
Different color schemes encode different visual emphasis. Grayscale spectrograms show structure clearly but compress dynamic range. Colored heatmaps (viridis, jet, magma) emphasize different parts of the energy distribution. CrowLingo's atlas uses an iridescent dark-mode palette designed to match the brand aesthetic, which means the visualization is partly an editorial choice rather than a pure signal display. Knowing the color scheme tells you what visual emphasis the author chose, and adjusting your interpretation accordingly is part of careful reading. Some published papers don't specify their color scheme; treat those visualizations with appropriate caution about dynamic range claims.
What spectrograms make you miss
Two things, mostly. First, anything that requires the actual waveform shape rather than the magnitude envelope — fine-grained timbre, phase relationships, transient details below the time resolution of the STFT window. Second, anything that requires reasoning about silence or absence — a shows present energy; absence is just dark pixels with the same color as background noise. Some bioacoustic findings depend on identifying calls that aren't present at certain times or in certain frequency bands, and that kind of negative observation is harder to do with a spectrogram than with raw waveform inspection.
Why they're still the standard
Despite all of this, spectrograms remain the standard visualization for sound for a simple reason: they make audio inspectable in a way that raw waveforms don't. A human looking at a waveform sees an oscillating line and gets very little structure beyond loudness over time. A human looking at a sees patterns — calls, calls, calls — that map directly to what the audio contains. The compromise of phase loss and time-frequency trade-off is worth it for the inspection capability. Just know what you're seeing, know what you're not seeing, and treat the visualization as a useful approximation rather than ground truth.