Audio to Mel Spectrogram

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Mudasser Ahmad
Mudasser Ahmad 2023-9-22
评论： Mudasser Ahmad 2023-9-22
Hello I am working on sound classification problem. my task is to create mel spectrogram with three different windows length 93ms and 46ms and 23ms this is achieved by keeping n_fft to 2048,1024 and 512 respectively. I am getting (128,216) but I don't understand the 3 there (128,216,3) here 128 is number of frequency bins and 216 are number of frames. Can some help me understand the right side the attached image the DL part?
  2 个评论
Mathieu NOE
Mathieu NOE 2023-9-22
You have 3 time windows , so you are omputing 3 spectrograms, each one is an array size 128 x 216
at the end your 3 spectrograms are stored in a 3D array, size 128 x 216 x 3
Mudasser Ahmad
Mudasser Ahmad 2023-9-22
Thanks for your feedback.
is my code doing correctly? this is what the image says?
import librosa
import numpy as np
# Load the audio file
y, sr = librosa.load(r'G:\A NEW RESEARCH DATASET\1Fire\2_Fire.wav') # Replace 'path_to_your_audio_file.wav' with your audio file path
# List of n_fft values
n_ffts = [2048, 1024, 512]
# List to hold spectrograms
spectrograms = []
#Generate spectrograms for each n_fft value
for n_fft in n_ffts:
mel_spec = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=n_fft, hop_length=512, n_mels=128)
spectrograms.append(mel_spec)
# Stack the spectrograms along the third dimension
tensor = np.stack(spectrograms, axis=-1)
print(tensor.shape) # This should print (90, time_steps, 3), where time_steps depends on the length of your audio file

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