As per my understanding, you are aiming to increase the resolution your result using the “wsst” function. Please note that modifying the “wsst” function can have unintended consequences, as it calls the “cwtfilterbank” function, which performs inclusivity checks on the “VoicesPerOctave” parameter. You can find its description in the documentation below under the output arguments and also in the “wsst” function’s code:
According to the documentation, the maximum and minimum scales specified by “VoicesPerOctave” are automatically determined by the energy spread of the wavelet in frequency and time; hence, the range is fixed between [1, 48]:
And it is possible to achieve more resolution by varying the sampling frequency and adjusting the “SamplingFrequency” and “Wavelet” parameters.
Increasing the “VoicesPerOctave” parameter can result in the computation of a large matrix that replicates the 1-D time series data D, over N_s, where N_s is the number of scales in obtained in the output of “wsst”. This leads to an output size of N_s x numel(D), which could be memory-intensive for large time series.
