Musical Instrument Sound Classification using CNN (Part 2/2)

Musical Instrument Sound Classification using CNN (Part 2/2)

Hello world, welcome to the second part! In the previous part, I wrote about data collection and data generation. Here in this part I wanna continue with features preprocessing, label preprocessing, model training and model evaluation respectively. Let’s get started!

Hello world, welcome to the second part!

In the previous part, I wrote about data collection *and *data generation. Here in this part I wanna continue with features preprocessinglabel preprocessingmodel training *and *model evaluation respectively. Let’s get started!

Step 3: Features preprocessing (using MFCC)

Raw audio wave that we extracted in step 1 using librosa is not really informative since it essentially only consists of one-dimensional data stored in an array. This array shape only represents the amplitude (loudness) of each bit. In fact, loudness is not the only feature that we want to take into account when we are about to distinguish different sounds. Instead, it is also necessary to consider the pitch of those audios. Therefore, in order to extract the pitch information based on given raw audio we are going to utilize a function called mfcc().

MFCC stands for Mel Frequency Cepstral Coefficients. There are so many papers out there related to sound classification and speech recognition which use this feature extraction method in order to obtain more information within audio data. In this article I will be more focusing on how the code work (since the math behind MFCC is very complicated — well, at least for me, lol). If you want to understand more about how to calculate MFCC I recommend you to read it from this page: https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html.

Anyway, remember our _generated_audio_waves _variable? Since it contains all the raw audio data, then we can simply use a _for loop _to iterate through all the values of the array and convert each of the waves into MFCC features. Here is my code for that:

mfcc_features = list()

for i in tqdm(range(len(generated_audio_waves))):
    mfcc_features.append(mfcc(generated_audio_waves[i]))
mfcc_features = np.array(mfcc_features)

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