Analyzing Audio using Machine Learning

Machine Learning is the evolution of model predictivity and implementation of statistics. We all use machine learning in our day-to-day lives. One of the tasks we regularly do is using virtual assistants Alexa, Siri, Google Home, etc. All these virtual assistants analyze audio using machine learning and deep learning. One of the tremendous aspects of this is Natural Language Processing (NLP) which helps in speech recognition. They extract information and data from the audio signals. It comes under the field of Automatic Speech Recognition (ASR) 

Types of Audio Formats

We as a human are always listening to sounds and we know how to distinguish them. All sounds contain specific information and data in them. We process these sounds and conclude the information from them.  We store sounds in many formats so that we can listen to them afterward to work on them. But one thing is sure that it is a wave-like format.

WAV (Waveform Audio File)

This is a subset of RIFF i.e., Resource Interchange File Format which is specifically used to store digital audio files. It stores the audio with different sampling bitrates and rates and it doesn’t even apply any compression on the bitstream. However, these files are larger than compared to MP3 because of their usage in CDs.

MP3 (MPEG-1 Audio Layer 3)

These files have to be compressed to their one-twelfth size while preserving the quality of the sound. These files end with the suffix “.mp3”. However, these files are usually first downloaded and then played rather than streaming them. To create these files we use two programs i.e., Ripper and Encoder.

WMA (Windows Media Audio)

It is under ASF (Advanced System Format) which warps the audio bitstream. It serves as an audio codec too. 

Useful Terminology

Sampling and Sampling Frequency

Reducing a continuous signal into a sequence of discrete values is Sampling. Similarly, the number of samplings taken over a subsequent time is the rate or Sampling Frequency. A low sampling frequency is fast and cheap to commute but has more information loss. However, a high sampling frequency is expensive to commute but has less information loss.

Amplitude

Change in measure of sound waves over a while is amplitude. 

Fourier Transform

The Fourier Transform decays an element of time (signal) into constituent frequencies. It shows the sufficiency (measure) of every recurrence present in the fundamental capacity (signal).

Periodogram

An estimate of spectral density in a signal is called Periodogram. The outcome of the Fourier transform can be can think of as a Periodogram.

Spectral Density

Power spectrum can be described as the power of distribution into components of discrete frequency while composing a signal. The statistical average of a signal estimated by the frequency content is known as Spectrum. However, the spectral density is the frequency content of the signal.

Handling the Data

While analyzing audio through machine learning we have to handle some unstructured data. Therefore, we have to go through some preprocessing steps before we jump to audio analysis. First, make sure the data is in machine-understandable format. Then we have to apply feature extraction and feature engineering to extract the underlying data. In this process, we find the components of a signal that can be separated from other signals.

We have to calculate MFCC while analyzing audio signals using Machine Learning.

Firstly, we should slice the signals into short frames. Secondly, with the help of a periodogram, we have to calculate the power spectrum for each frame. Afterward, apply the mel filterbank on the power spectra and sum the energy for each filter. And then we just have to take the DCT of the log filterbank energy.

Real-world  Applications 

• Searching song name with the help of music.
• Recommending songs via a virtual assistant
• Analyzing your commands with a virtual assistant like Siri
• Recommending songs on a radio channel

What did we learn?

To sum up we learned how analyzing audio works with machine learning. We saw how models can separate data from digital audio signals. All machine learning models need preprocessing of data before diving into the core concept. Preprocessing and basic terminologies should always be clear before implementing any machine learning project. Natural Language processing plays a major role in recognizing data from audio signals. It is used for text recognition as well. Machine Learning is changing the world at a pace we never have thought of.

Sources 

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