Answers.Body: <p>I assume, you question means: have scalograms an advantage over spectrograms in bioacoustics?</p>
  <p>I see two main differences:</p>
  <ul>
  <li>spectrograms (FFT based) have constant frequency and temporal resolution</li>
  <li>scalograms (wavelet based) have logarithmic frequency and temporal resolution</li>
  </ul>
  <p>spectrograms:</p>
  <ul>
  <li>are good for high-frequency features, where temporal scale fits with acoustic feature</li>
  <li>are bad for low-frequency features, as temporal scale may be too short for acoustic feature</li>
  <li>have constant (filter) group delays</li>
  </ul>
  <p>scalograms:</p>
  <ul>
  <li>compress high-frequency spectral features</li>
  <li>have higher spectral resolution at lower frequencies</li>
  <li>have frequency dependent (filter) group delays</li>
  </ul>
  <p>The major advantage of spectrograms is the use of FFT but otherwise scalograms  convey the biological information better. In particular, they reflect the logarithmic nature of most auditory processing.</p>
  <p>Very often, I see that spectrograms are transformed to logarithmic frequency scaling or also to nearly logarithmic Mel scale, but these methods do not consider the also desired (required?) change in temporal scale, which is absent in spectrogram.</p>
  <p>Coming back to OPs question: If you are investigating biological relevance of acoustic features or are interested in low frequency details, e.g. for discrimination or classification purpose, then yes, scalograms make more sense than spectrograms.</p>
  <p>Also, if you wanted to have more or less uniform distribution of spectral information (reduce redundancy) then scalograms are better. In fact, neural-network based audio processing transform data first to Mel scale.</p>
  <p>I myself, use frequently Gabor wavelets as they are constructed in closed form and have not used yet Daubechies wavelets. So, there may by more aspects to discuss.</p>
  
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Answers.Text: Thank you WMXZ. Great answer. I learned a few things.
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Body: <p>What would be the main advantages of wavelets, such as Daubechies, over an FFT analysis for researching cetacean vocalizations?</p>
  <p>Would one of the advantages be the exposure of micro-structures in acoustic signals, if any are present? By micro-structures, I refer to possibly very small acoustic structures that would not be apparent in plain FFT analysis and that could be significant for the cetaceans producing them.</p>
  <p>I am asking because I am a programmer who dabbles in real-time bioacoustics applications and I am considering developing customized software using wavelets (possibly Daubechies), if such a significant effort has any potential benefit.</p>
  
Tags: <cetacean><discussion><fft>
Title: Wavelets Versus FFT for Cetacean Vocalizations
ThreadText: # Wavelets Versus FFT for Cetacean Vocalizations
  
  (Asked by: username_0 on 2022-07-16)
  
  What would be the main advantages of wavelets, such as Daubechies, over an FFT analysis for researching cetacean vocalizations?
  
  Would one of the advantages be the exposure of micro-structures in acoustic signals, if any are present? By micro-structures, I refer to possibly very small acoustic structures that would not be apparent in plain FFT analysis and that could be significant for the cetaceans producing them.
  
  I am asking because I am a programmer who dabbles in real-time bioacoustics applications and I am considering developing customized software using wavelets (possibly Daubechies), if such a significant effort has any potential benefit.
  
  **Tags:** cetacean, discussion, fft
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-07-17. Score: 12 | Accepted answer
  
  I assume, you question means: have scalograms an advantage over spectrograms in bioacoustics?
  
  I see two main differences:
  
  * spectrograms (FFT based) have constant frequency and temporal resolution
  * scalograms (wavelet based) have logarithmic frequency and temporal resolution
  
  spectrograms:
  
  * are good for high-frequency features, where temporal scale fits with acoustic feature
  * are bad for low-frequency features, as temporal scale may be too short for acoustic feature
  * have constant (filter) group delays
  
  scalograms:
  
  * compress high-frequency spectral features
  * have higher spectral resolution at lower frequencies
  * have frequency dependent (filter) group delays
  
  The major advantage of spectrograms is the use of FFT but otherwise scalograms convey the biological information better. In particular, they reflect the logarithmic nature of most auditory processing.
  
  Very often, I see that spectrograms are transformed to logarithmic frequency scaling or also to nearly logarithmic Mel scale, but these methods do not consider the also desired (required?) change in temporal scale, which is absent in spectrogram.
  
  Coming back to OPs question: If you are investigating biological relevance of acoustic features or are interested in low frequency details, e.g. for discrimination or classification purpose, then yes, scalograms make more sense than spectrograms.
  
  Also, if you wanted to have more or less uniform distribution of spectral information (reduce redundancy) then scalograms are better. In fact, neural-network based audio processing transform data first to Mel scale.
  
  I myself, use frequently Gabor wavelets as they are constructed in closed form and have not used yet Daubechies wavelets. So, there may by more aspects to discuss.
  
  > **Comments:**
  > **username_0**: Thank you username_1. Great answer. I learned a few things.  
  
  ---
  
  