As we learned in Part 1, the common practice is to convert the audio into a spectrogram. The bit-depth influences the resolution of the audio measurement - the higher the bit-depth, the better the audio fidelity.īit-depth and sample-rate determine the audio resolution (Source) Spectrogramsĭeep learning models rarely take this raw audio directly as input. For example, a bit-depth of 16 means that the amplitude number can be between 5 (2 ¹⁶ - 1). The bit-depth tells us how many possible values those amplitude measurements for each sample can take. Given the sample rate, we can figure out at what time instant each amplitude number measurement was taken. Since the measurements are taken at fixed intervals of time, the data contains only the amplitude numbers and not the time values. For instance, if the sample rate was 16800, a one-second clip of audio would have 16800 numbers. In memory, audio is represented as a time series of numbers, representing the amplitude at each timestep. This array looks the same no matter which file format you started with. When the file is loaded, it is decompressed and converted into a Numpy array. When that audio is saved in a file it is in a compressed format. The metadata for that audio tells us the sampling rate which is the number of samples per second. Play audio in a notebook cell (Image by Author) Audio Signal DataĪs we saw in the previous article, audio data is obtained by sampling the sound wave at regular time intervals and measuring the intensity or amplitude of the wave at each sample. If you are using a Jupyter notebook, you can play the audio directly in a cell. Visualize the sound wave (Image by Author)Īnd listen to it. Or, you can also do the same thing using scipy: They all let you read audio files in different formats. It doesn’t have as much functionality as Librosa, but it is built specifically for deep learning. If you are using Pytorch, it has a companion library called torchaudio that is tightly integrated with Pytorch. Librosa is one of the most popular and has an extensive set of features. Python has some great libraries for audio processing. From listening to sound recordings and music, we all know that these files are stored in a variety of formats based on how the sound is compressed. Automatic Speech Recognition ( Speech-to-Text algorithm and architecture, using CTC Loss and Decoding for aligning sequences.)Īudio data for your deep learning models will usually start out as digital audio files.Foundational application for a range of scenarios.) Audio Classification ( End-to-end example and architecture to classify ordinary sounds.Feature Optimization and Augmentation ( Enhance Spectrograms features for optimal performance by hyper-parameter tuning and data augmentation).What are Mel Spectrograms and how to generate them) Why Mel Spectrograms perform better - this article ( Processing audio data in Python.What are Spectrograms and why they are all-important.) What problems is audio deep learning solving in our daily lives. State-of-the-Art Techniques ( What is sound and how it is digitized. My goal throughout will be to understand not just how something works but why it works that way. Here’s a quick summary of the articles I am planning in the series. Since data preparation is so critical, particularly in the case of audio deep learning models, that will be the focus of the next two articles. Now that we know how sound is represented digitally, and that we need to convert it into a spectrogram for use in deep learning architectures, let us understand in more detail how that is done and how we can tune that conversion to get better performance. This is the second article in my series on audio deep learning.
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