When you position the line above a specific frequency on the spectrogram, a panel on the left side of the screen displays the octave, note name, and MIDI number. In frequency mode, when you touch the frequency scale on the right of the screen a blue line appears on the spectrogram. This feature alone makes almost all other oscilloscope apps unnecessary because it means you can easily see ANYTHING that you need to see. When you get to the event you want to see you can pinch to zoom in right down to sub-sample level of detail, then zoom back out when you are done and zoom in on something else. You never have to worry about trying to catch short events that disappear quickly because you can watch the sounds go by slowly at a 3 second viewer scale, then just pause and scroll back if you missed something. We can't emphasize enough how useful this is. WHAT DOES THIS APP DO THAT OTHERS DO NOT? When zoomed in, the oscilloscope uses sinc upsampling to show you a close approximation of what actually outputs from your audio interface after digital to analog conversion, rather than just displaying raw audio samples. Bark-frequency scale spectrogram shows nice detail at low and high frequencies Automatically adjusts FFT size to fit zoom setting Level based trigger mode has lookahead, pause, and zoom Frequency trigger mode helps identify the precise frequency and MIDI note of sounds in the spectrogram See both oscilloscope and spectrogram separately or overlayed together Pause and scroll horizontally to browse back in time View up to three seconds of audio on the screen Run as both Audio Unit and standalone app Y =(imageData, sr=44100, hop_length=512, fmin=None, bins_per_octave=12, tuning=None, filter_scale=1, norm=1, sparsity=0.OSCILLOSCOPE & SPECTROGRAM is a powerful audio analyzer app and audio unit plugin Gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) Plt.savefig(path,transparent = True, bbox_inches = 'tight', pad_inches = 0) Hope its clear now.Дata_aud,sr_aud = (path)Д¬qt = (data_aud, sr=44100, hop_length=512, n_bins=84, bins_per_octave=12, tuning=None, norm=1, sparsity=0.01, window='hann', scale=True, pad_mode='reflect') Even if I do reconstruction in matlab, how would i tackle the different dimensions. I am using Python and librosa library for all the operations. How would one really reconstruct back the audio signal with this criteria.ДЄm i doing anything wrong. I know we have to make use of phase information to reconstruct the signal. I have the phase information of 84*260 resulted from CQT in the beginning. To reconstruct back to audio domain time signal, first i will read the image, when i read my image I get 224*341*3 values. So when i read this saved image I get 224*341*3.ДЄfter Image processing work, Now I want to reconstruct back my audio time domain signal to check my work. png image, to do some image processing work. Now, the CQT values consists of complex values(84*260), but python makes use of only magnitude values to plot. I have applied transformation(Constant Q Transformation) to my audio time domain signal, this gives me Frequency vs Time representation.

Audio spectrogram software