How to change the spectrogram magnitude to linear? (2024)

FAQs

How is a spectrogram calculated? ›

Spectrograms are normally computed using the Short-Time Fourier Transform (STFT), and can be done in real-time. It consists of splitting the signal into overlapped frames, using a window function (typically scaled cosine like Hann) and then computing the FFT for the frame.

With "pspectrum" the unit is expressed as db and with "spectrogram" the unit is expressed as db/Hz.

stft focuses on the FT of windowed and segmented (overlaped) data and the output can be used to reconstruct the original (under certain condition). spectrogram focuses on the spectral estimation based on STFT.

The magnitude spectrogram is computed by framing the signal into short time windows, applying a hamming (or similar) window, computing the fft over each window, and taking the absolute value of the complex numbers obtained.

Bandwidth The bandwidth depends on all three parameters (FFT length, Frame size, Window) and the sampling frequency of the sound file.

In the spectrogram view, the vertical axis displays frequency in Hertz, the horizontal axis represents time (just like the waveform display), and amplitude is represented by brightness. The black background is silence, while the bright orange curve is the sine wave moving up in pitch.

In a spectrogram, the horizontal dimension represents time and the vertical dimension represents frequency.

A spectrogram uses color to show the intensity (or loudness) of sound at various frequencies (or pitches) over time. Vertically you can see whether sound is more or less intense at different frequencies. Lower on the vertical axis are lower frequencies, higher on the vertical axis are higher frequencies.

A waveform tracks changes in air pressure over time as a sound is produced. The bottom display shows a spectrogram. A spectrogram provides information about the acoustic components of a sound. (We will learn about these acoustic components in more detail throughout the semester.)

A spectrogram illustrates how the constituent frequencies of a signal vary over time. This application generates the spectrogram of several audio files, including a DTMS tone, human voice saying “MapleSim”, violin note played with vibrato, and an entire violin scale, C8 piano note, series of dolphin clicks, and more.

How to normalize spectrogram in MATLAB? ›

s = spectrogram( x ) returns the Short-Time Fourier Transform (STFT) of the input signal x . Each column of s contains an estimate of the short-term, time-localized frequency content of x . The magnitude squared of s is known as the spectrogram time-frequency representation of x [1].

s = spectrogram( x , window ) uses window to divide the signal into segments and perform windowing. s = spectrogram( x , window , noverlap ) uses noverlap samples of overlap between adjoining segments. s = spectrogram( x , window , noverlap , nfft ) uses nfft sampling points to calculate the discrete Fourier transform.

A spectrogram can be generated by an optical spectrometer, a bank of band-pass filters, by Fourier transform or by a wavelet transform (in which case it is also known as a scaleogram or scalogram).

In the spectrogram view, the vertical axis displays frequency in Hertz, the horizontal axis represents time (just like the waveform display), and amplitude is represented by brightness. The black background is silence, while the bright orange curve is the sine wave moving up in pitch.

To generate a spectrogram, a speech signal is divided into frames which are multiplied by a Hamming window as shown in Fig. 6. Each windowed frame is transformed into the frequency-domain from time-domain by applying fast Fourier transform (FFT).