I’m trying to make a neural network that tells the accent of the person who is speaking, but i need to convert the audio into numbers. How could i do that?
hi @simparosow , welcome to the forums!
To convert an audio signal into a format that a neural network can process, you’ll typically need to do a process called “feature extraction.” There are many different techniques for feature extraction, but one of the most common is to use a technique called “Mel-frequency cepstral coefficients” (MFCCs).
Here’s a rough overview of how this process might work:
- Use a Python audio library (such as librosa or PyAudio) to load the audio file into your Python script.
- Split the audio signal into small “frames” (usually around 20-30 milliseconds long each).
- For each frame of audio, compute the MFCCs. This involves taking the short-term Fourier transform (STFT) of the audio signal, applying a filter bank that models the human auditory system, and then computing the discrete cosine transform (DCT) of the logarithm of the filter bank outputs.
- Concatenate the MFCCs from each frame together to create a “feature vector” for the entire audio file.
Once you have a feature vector for the audio file, you can use it as input to your neural network. The exact architecture of the neural network will depend on your specific use case, but you might start with a simple deep neural network (DNN) architecture.
Keep in mind that feature extraction for speech recognition is a complex task, and the specific details will depend on the specific use case. You may need to experiment with different feature extraction techniques and neural network architectures to find the best combination fo
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