Not entirely sure if this Stack or a ML-related stack would be a fit for this question, but I focus on the signal processing here on this question, so I decided this one
I'm developing a speech classification system. Imagine, that you have a dataset of happy and sad speech, and you want to classify them. I'm looking for a minimum set of standard pre-processing steps that I have to do to compare happy and sad speech and build a machine learning-based classifier out if it. This is something, that I believe is called normalisation in the literature, it is definitely expected to be done, and I find it confusing.
To contrast, in image-based problems, I find that most "normalisation" can be safely neglected. In the case of audio, I'm not sure if it can be neglected, and what is a proper way to do normalisation. I tried to read more about this, but I find even the definition of normalisation ambigous in audio, i.e:
- Some people refer to normalisation as preprocessing the two classes of data so they are comparable. Sometimes this is straightforward, but in speech classification problems, I certainly don't think it is.
- Normalisation has a different meaning when we refer to audio normalisation. Wikipedia link
- Normalisation as standardisation (MeanStd scaling) or normalisation (MinMax scaling) is also a predominant definition.
There are many techniques that fit the normalisation bill then. I'm not aiming to give a complete list, but here is a few
- Channel normalisation.
- Loudness normalisation
- Voice activity detection (cutting out silence)
- Speech pseudonymisation
- Cepstral mean and variance normalisation
What is the minimum I'm expected to do when I'm building a classifier for happy/sad speech detection?
