markadivalerio / audio-classifier-project Goto Github PK
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Project for UT Scalable Machine Learning Fall 2019
Greetings,
Hope you have a nice day, i am working on an audio classification project which i have to distinguish between very similar sounds so i thought about your approach of using the multiple futures but didn't know how, so far i used only mel-spectgrom and barely got a 32% error rate with image classification (using fastai)
so i want to try your model with my data if that is possible but, i didn't understand the code that much.
i wish to contact you in order to help me setup the code, here is my discord
(Artist201#5802)
hope to meet you as soon as possible
Thank you.
i see in feature extraction after this piece of code
audio_data, sample_rate = librosa.load(file_name) stft = np.abs(librosa.stft(audio_data))
that you some time use stf and some times use audio_data
like
mfcc = np.mean(librosa.feature.mfcc(y=audio_data, sr=sample_rate, n_mfcc=40).T,axis=0)
features.extend(mfcc) # 40 = 40
chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
features.extend(chroma) # 12 = 52`
why is that ? what is the difference ? would like you to elaborate more on this. :)
Thank you !
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