Spektrogrammia

Spektrogrammia is a multidisciplinary field focused on the creation, analysis, and interpretation of spectrograms—time-frequency representations of signals. The term describes systematic work with spectrogram data across domains rather than a single technique.

Core activities include selecting analysis methods such as the short-time Fourier transform, wavelet transforms, or Mel-scaled spectrograms, and computing representations of signals over time and frequency. Researchers extract features from spectrograms, including spectral centroid, bandwidth, MFCCs, and chroma descriptors.

Applications span audio and music (speech recognition, speaker identification, instrument classification, music information retrieval), biomedical signals (EEG and ECG analysis), and engineering or environmental monitoring (machinery fault diagnosis, vibration analysis, acoustic scene classification).

Workflow typically involves data acquisition and pre-processing, spectrogram computation with chosen window length and overlap, normalization or denoising, followed by interpretation or quantitative modeling. Visualization supports hypothesis generation and communication.

Key challenges include the time–frequency trade-off, sensitivity to noise and artifacts, and the risk of misinterpretation without domain knowledge. Reproducibility depends on reporting analysis parameters such as window type, size, overlap, and scaling. The term is not uniformly standardized in peer-reviewed literature and is mainly used in interdisciplinary or applied contexts. Tools include Python libraries (Librosa, SciPy), MATLAB toolboxes, and R packages.