I have a signal (audio - voice) with 1 second of duration with sample rate of 50000 Hz. It is big signal and I wish extract some features and apply pattern recognition or classification.
My question is if the Wavelet transform or Discrete Wavelet transform is a time frequency representation (or time scale). So I shoudn't use window in signal as a buffer or like STFT? Or I should use window like STFT with hop_size and apply to every window a wavelet transform?
I think STFT use window to localize signal in time and see frequency content. Wavelet doesn't need this approach.
I try to compare this feature extraction with well know mel frequency spectrogram or Mel-frequency cepstral coefficients (MFCCs).
Sorry if there is any answer on this, I haven't found it.
(taking advantage of the opportunity if anyone wants to explain to me how filter bank (or discrete wavelet) located spectral content in time. Is it property of convolution?)