Audio Deep Learning Libraries

Table of Contents

Introduction:

Audio processing in machine learning refers to the usage of ML techniques and algorithms to analyze and process audio data to gain informative insights from it. Some popular ML algorithms used in audio processing include convolutional neural networks(CNN), recurrent neural networks(RNN), and deep learning algorithms. Feature extraction techniques like Mel Frequency Cepstral Coefficients(MFCCs) and spectrograms are often used with these algorithms to enhance efficiency and accuracy of these algorithms.

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The deep learning algorithm’s accuracy over a period of time has improved drastically. As a result, we no longer need traditional audio processing techniques in order to work with audio data. Instead, rather than using raw data in the form of audio files, deep learning has achieved the smartness to convert the audio data into images and then apply CNN models to gain insights from them. This task is accomplished by generating spectrograms from the audio dataset.

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Spectrum & Spectrograms:

In a real-life scenario, signals of multiple frequencies get added together and form a composite signal. A spectrum is a set of frequencies combined together to produce a signal. Since the signal produces a frequency that varies over time, so is the variation in the spectrum with respect to time.

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Spectrograms are the pictorial representation of signals over time, a time v/s frequency plot. Each vertical disturbance shows the signal’s spectrum at that instant in time.

The first image below shows the Amplitude v/s Time graph, which tells about the loudness of the sound at any instance of the time. While the second image is of a spectrogram relative to the signal’s frequency. These spectrograms are created by the Frontier Transforms which decomposes any signal into its constituent frequencies.

Python provides us with a bunch of library packages that perform audio signal processing, music information retrieval, audio enhancement, speech modulation, music synthesis, etc…

1.Librosa – Python library for music and audio analysis. Primarily built for music and music information retrieval(MIR) system.

2.IPython.display.audio – To directly play audio in jupyter notebook.

3.TorchAudio – Primarily an ML library.

4.PyAudio

5.SoundFile – Used to read and write sound files.

6.Essentia – Used in research activities of Music Information Retrieval, it caters to needs for both the rapid prototyping and large-scale analysis.

7.pyAudioAnalysis – Audio-related functionalities like feature extraction, classification, segmentation, and visualization.

8.pydub – Library to work only with .wav files.

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