johnmartinsson/differentiable-mel-spectrogram
The official implementation of DMEL the method presented in the paper "DMEL: The differentiable log-Mel spectrogram as a trainable layer in neural networks".
This project helps audio machine learning engineers and researchers by providing a trainable, differentiable log-Mel spectrogram layer for neural networks. It takes raw audio data as input and produces optimized Mel spectrograms, which can then be used for tasks like audio classification or sound event detection. The primary users are researchers and practitioners working on deep learning models for audio processing.
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Use this if you are a deep learning engineer or researcher looking to incorporate a learnable Mel spectrogram transformation directly into your neural network architectures to potentially improve audio classification or other audio analysis tasks.
Not ideal if you are an end-user simply needing to generate standard Mel spectrograms or if you are not working with neural networks for audio processing.
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Language
Python
License
Apache-2.0
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Last pushed
Dec 21, 2024
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