What is powering the onslaught of Artificial Intelligence in every industry across the world?
It is Machine Learning!
In very simple words, you teach the machine how to derive results. The results purely depend on algorithms used and the data that is poured to train/teach the machine. Machine learning is being used to power recommendation systems, audio/video classification software, autonomous driving, and many more industrial processes. There are 97 million songs in the world, now these are just the songs that are documented. With the rise of music apps, this count continues to increase exponentially. Even if we go with this figure, let us say that every song is just 10MB of data then we end up with 970TB of data that has to be analyzed and labeled.
Content management becomes the need of the hour due to the rapidly increasing data. An ML-based audio classification tool helps to manage audio content like never before. The need to efficiently manage the audio content becomes necessary with the ever-increasing data. Moreover, recommending songs on the basis of the user activity is a very common use case of a recommendation system.
With the rise of music streaming services, an efficient music classification tool is necessary to be able to classify songs under relevant categories and genres. Obviously, the classification can be done manually too, by the people uploading the songs but this method is not scalable. Thus, an AI-based music classification tool can work wonders when it comes to classifying songs.
What has Queppelin developed?
Treading on the same lines, Queppelin has made an AI-based tool for Music Classification. We listen to a lot of songs daily, but we are always confused about what to listen to next. The ML audio classification tool analyzes songs and groups them according to 10 genres and various other attributes. Queppelin has used cutting-edge ML technology and done various experiments to get to a model that analyses genre at 77% accuracy levels and recommends the right music according to the user’s listening habit. The tool is capable of performing the following:
- Analyse the genre of a song based on parameters like Spectral Centroid & Rolloff, Chromagram, Spectrogram, BPM and many other parameters.
- Generating the playlists of similar genre
- Music streaming platforms similar to Spotify, Youtube Music etc can use this platform to show related music content
- Categorizing the music based on a particular genre
How does this tool work?
Music recommendation systems are the basis of music-providing applications. The better their recommendation algorithm, the more users prefer it. Music recommendation saves the users from making time-consuming decisions as to which song to listen to next. Queppelin has used the following libraries:
- Librosa library for music analysis
- Numpy and Pandas for data cleansing and mathematical operations
- Scikit for data handling and analysis
- Keras for training and creating the ML model.
The tool calculates various properties of the song, labels them, and trains the ML model on the same data. Once the model gets trained and learns from the features, we can always use it to classify any new song under one of the 10 genres for which the model is trained.
Benefits of going for a tool instead of reinventing the wheel
There are many music applications in the market and they charge a lot of money for premium services. There is always space for new players to democratize the markets. Companies that own music applications have dedicated teams working on custom ML models to recommend better music. If any business wants to get into the music industry, they’ll have to build teams from scratch, which can be pretty expensive. Instead of that, they can accelerate their product in the market by using the Saas tool, this will help them to integrate a recommendation system in their product easily. Recommending similar songs to users, making custom playlists for users will become easy and they won’t have to deal with the inner workings of the tool.
Spotify reported that over 60,000 songs are released every day, that is approximately 1 song every second. Now imagine how hard it would be to classify songs if the data is being generated at such a high rate.
The process we went through
Apple Music, Spotify, YT music use sophisticated ML models to make the user experience more conducive. They use Supervised learning algorithms to make recommendations better, even reinforcement learning algorithms are slowly coming into play. Queppelin went through the following steps to develop this AI tool.
- Identifying the problem.
- Collecting and cleaning the data.
- Converting the data so that it is ingestible for the ML algorithm.
- Training the model.
- Feature Engineering.
- Evaluating and improving the model.
- Serving the model to the clients.
What else can our ML model help you with?
Our ML model can help you out with obscene content detection in videos, so no one has to sift through the videos manually to detect obscene material. It will save a lot of time and effort for the organization.
We also know that half of the content generated on social media is spam, going through tons of written data becomes very tiring and a human tends to make mistakes. Our ML model can help you with spam detection.
Also, you can use the ML model to categorize the sentiment of videos, music, and text. There is a lot of upside to getting to know about the sentiment of data as it can help companies automatically improve their campaigns and customer experience.
Why use our tool?
The market is becoming competitive day by day and every business is focusing on how to bring their product as fast as they can into the market. But faster doesn’t mean better. The product has to also go through quality checks. To ensure that the users like the product the technology used have to be feasible. The product needs to be smooth. AI tools, like the one developed by Queppelin, have made the life of business owners easy, as they don’t have to go searching in the market for people, all they need is at their hands and ready to use.