“Music expresses that which cannot be said and on which it is impossible to be silent. -Victor Hugo
Music has been known to humans for times immemorial. Ever since the melody of instruments has fallen on the ears of humans, it has implanted the seeds of emotions that are otherwise hard to achieve.
While music is altogether a melody that connects all hearts in this world, there are further classifications of music that bind together music lovers and keep the melody going on.
Music Genre Classification or classification of music into different categories or genres is a concept that helps the masses to differentiate between 2 genres based on their composition or the beats they withhold. In recent times, music genre classification has become a very popular concept as more and more genres are emerging around the world.
From K-Pop to Jazz, music lovers rely on the technology of music genre classification and are able to listen to songs as per their preferences. While it takes only a click for a listener to switch from Jazz music to Rap, there is certainly much more beneath the surface that fuels our love for music.
That said, this blog will be throwing light on the top 10 music genres and the way they are classified with the help of Machine Learning and Artificial Intelligence.
(Must read: IoT in Music Industry)
Classifying music genres is a complicated task. Yet, technology has made this process automated and today millions of music applications are classifying music in different genres within a span of seconds.
“If I were not a physicist, I would probably be a musician. I often think in music. I live my daydreams in music. I see my life in terms of music.” ― Albert Einstein
But, before learning how it is done, let us first discover the top 10 popular music genres prevalent in the contemporary music industry.
Hip Hop & Rap
Categorized music genre
Have you ever wondered how machines generate music genres? Ever thought about the technology that makes your smartphone application produce songs when you enter pop songs? The answer to all these questions is Machine Learning models.
Music Genre Classification using Machine Learning is what we will be discussing in this segment. Before understanding music genre classification, we shall first look into the definition of machine learning.
A technology of Artificial Intelligence, Machine Learning is a concept wherein computers or machines learn from information (data) fed to them. Based on data that is entered into the machines, Machine Learning helps computers to build interpretive patterns and build analytical models automatically. Learn more about this through Machine Learning Tutorial)
Music Genre Classification using Machine Learning is a comparatively newer concept that has emerged on the surface in recent times. While music genres have been known to the world for decades, machines have been able to work along the lines of music genre classification in the contemporary world where every other person is listening to music.
As computers and smartphones have become the new music equipment, Machine Learning has facilitated the classification of music genres using several techniques. We will be discussing the three most common techniques of music genre recognition through Machine Learning.
(Also read - Top Machine Learning Tools)
One of them, K-Nearest Neighbour (KNN), is a technique that has been reportedly successful in categorizing music into different genres. Let us find out how.
A supervised machine learning algorithm, the K-Nearest Neighbour technique is used to find solutions for classification and regression problems. Relying on labeled input data to process unlabelled data in the future, this ML technique is used in music genre classification.
" The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other. " KNN Algorithm - ML
The KNN algorithm, when implemented in music genre classification, looks at similar songs and assumes that they belong to the same category because they seem to be near to each other. Among various other techniques that prevail in this concept, the best results have been procured out of this technique.
One of the simplest ML techniques, the KNN algorithm interprets data in a manner such that when the new data is fed, the machine automatically recognizes it and categorizes it as per the similarity of features.
What's more is that a particular set of traits of a particular music genre make it different from others which, in turn, helps machines to readily classify new data inputs.
Here we will understand the step-by-step process of music genre recognition. In the process of music genre classification, any technique of Machine Learning constitutes 5 steps. These are as follows:-
The prerequisite data involves past datasets that are required by machines to analyze past information and build analysis on that basis. The prerequisite for machine learning, in this the most necessary step involved in preparing machines for music genre classification using Machine Learning Algorithm.
The theoretical foundation of this concept implies that different techniques of Machine Learning can be incorporated to classify music into different genres based on pre-data sets and their subsequent analysis. It is important for us to understand the theory of music genre classification before proceeding to the next step. The KNN algorithm, which is considered to be the most successful algorithm, in this case, is involved in this theory.
Data Preprocessing Analysis
The Data Preprocessing Analysis involves machines scanning through the prerequisite data and analyzing them for procuring patterns. This stage particularly helps computers to study data and build patterns so that it leads to successful testing.
The training dataset involves feeding the machine with new data that will test the efficacy of the algorithm. The music genre classification dataset can be procured in any manner since it only requires random songs that can be classified by the algorithm into different genres.
Testing New Inputs
The last step, testing new inputs, refers to the machine working on the training data and testing data so that the algorithm can now filter music and carry out the task of music genre recognition. This step will provide results that will ultimately reveal the success of this algorithm.
(Learn more about data analytics and its types)
Another such algorithm is the Convolutional Neural Network Algorithm (CNN). The CNN algorithm in Machine Learning implies that machines process and interpret visual images based on which they predict future datasets.
Analyzing images and using visual recognition to learn information, machines uphold the value of these datasets that are helpful for classifying future information.
When it comes to music genre classification using neural networks, CNN enables machines to study spectrograms (visual graphs of music frequencies) which, in turn, helps machines to identify the category of specific music.
CNN algorithm in image processing is extremely helpful as it quickly scans through the spectrum of music frequencies and further classifies the data as required.
Herein, the role of the training process is immensely valuable. Since the machines require to be trained in terms of genre classification, the training process helps them to analyze and learn about different genres and the music pieces designated to each of the genres.
While the data analysis process takes place on one hand, on the other hand, computers generate patterns of information that help them memorize information in a way that will help them with new inputs. This way, past records lead to predictive patterns.
The third algorithm, Support Vector Machines (SVM), is a supervised learning algorithm that can be used in problems related to classification and regression analysis. A model of binary classification in machine learning, SVM majorly functions in binary classification, focusing on the segregation of data into 2 classes at a time.
Since this method supports the classification of data into 2 different classes, one can incorporate the SVM technique to segregate songs into 2 music genres at a time.
For instance, 10 songs could be processed at a time and SVM empowered computers can either filter them into the Pop category or Classical category. What's more, Multiclass SVM can also be used in this case to filter songs into more than 2 music genres at a time.
(Related blog: Support Vector Regression)
Let us understand the connection between Music Genre Classification and Machine Learning with the help of a case study.
Spotify is an audio streaming and media software application that was launched in 2006 in Stockholm, Sweden. The bond of Spotify and Machine Learning goes a long way as the application has incorporated AI technology into various domains of its operations.
Yet, one of the most striking aspects of Machine Learning in the functioning of Spotify is its music genre recognition ability. The spotify artificial intelligence is focused on delivering its media and audio services on the basis of customer demand and improving it from time to time.
"Using a tool called machine listening, songs are analyzed by Spotify’s music-intelligence division, the Echo Nest, based on their digital signatures for a number of factors, including tempo, acoustic-ness, energy, danceability, strength of the beat and emotional tone."Spotify and Music Genre Recognition
( Must read- How Spotify uses ML models )
In the end, the classification of music genres using various Machine Learning algorithms is a complex yet compatible process that helps software applications to categorize millions of songs using schemas formulated by pre-data sets.
(Also check: Role of Big data in Spotify)
It is extremely surprising that technology has percolated deep into our lives, and even in the field of music. Enabling machines to carry out music recommendation services and music genre recognition, Machine Learning has a vast application in our day-to-day lives. So the next time when you look for Rap songs, do not forget to think of Machine Learning algorithms that are doing the actual magic behind the curtains!
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