As an entrepreneur, doing this kind of not-so-glorious thing to promote my own features, I already feel quite pathetic about myself. Being pointed out by rwl4, I can also take a deep breath. I would like to apologize to the people who have been disturbed, and also apologize to the platform.
I am an entrepreneur, and my startup team is about to fail. In response to the questions raised by rwl4, I am very sorry. All the features on our website are completely free for everyone to use. If we can help some people, it will be a small consolation for the team before the failure. I apologize again.
I am an entrepreneur, and my startup team is about to fail. In response to the questions raised by rwl4, I am very sorry. All the features on our website are completely free for everyone to use. If we can help some people, it will be a small consolation for the team before the failure. I apologize again.
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Audio signal processing plays an important role in various applications such as speech recognition, music information retrieval, and speech synthesis. Among them, Mel spectrogram is a commonly used frequency domain feature representation method, which describes the sensitivity of the human auditory system to frequency. In this article, we will conduct performance tests on three commonly used audio processing libraries - audioflux, torchaudio, librosa, and essentia - to evaluate their efficiency in computing Mel spectrograms.