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you can extract most of the well-known features such as MFCC, filter bank and so on .. using speechpy or librosa libraries. by using them, you can extract any features from *.wav file. the list of functions is here.you can choose the best of them depending on your need. for example, you can try for MFCC by this code. also, it is very easy by speechpy ...


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What you are trying to archive is very hard if at all possible. The pinnacle of research today (including my own) is striving for good results on such tasks. DCASE challanges engage with similar tasks with some interesting results, though the make assumption which cannot be generalized to your case. In DCASE2019 task 3, for example, they assume up to 2 ...


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TL;DR You can use a speech recognition software to recognize the beginning of your recording or a speech recognition API to code this. If You want to implement this yourself, you can use a VAD algorithm to isolate the note at the beginning of the file, and then apply by a speech recognition method for classifying into a category. Edit: With respect to ...


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Hello check this source To begin with, let’s remember what the fundamental frequency is and in what tasks it may be needed. The fundamental frequency, which is also referred to as F0, is the vibration frequency of the ligaments when pronouncing voiced sounds. When pronouncing unvoiced sounds, for example, by whispering or uttering hissing and ...


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