Thank You, Next: How Machine Learning Is Revolutionising The Way We Make Music

Luke Frost our consultant managing the role
Author: Luke Frost
Posting date: 2/7/2019 9:15 AM
From Vinyl to Tidal; we all know that the way we consume music has changed. Technological advances have made Steve Job’s claim that he would put “1,000 songs in our pockets” seem antiquated, whilst Spotify’s algorithms serve us tracks that we’ll love before we’ve discovered them ourselves. 

But can the technologies that have brought us these advancements change the way we make music? Whether it’s leading to new instruments or creating a song without our input, Artificial Intelligence is a game changer. 

Make Some Noise


Until recently, the best way to imitate a sound was by experimenting with the different settings on a keyboard. However, this is no longer the case, thanks to Google’s research arm Magenta. They’ve created the NSynth Super, an instrument that generates sounds based upon Deep Neural Network techniques. 

These algorithms allow the NSynth to not only imitate a sound, but consistently learn more and more about the specificities of that pitch, creating something closer to reality. Users can then combine those individual sounds to create something unique and entirely original. This is potentially just the beginning of a new wave of music, and in a decade’s time the NSynth could end up having as big an impact as autotune. 

Talking About AI Generation


Whilst we’re still waiting to see the impact of instruments akin to the NSynth, machine-led compositions are becoming more and more commonplace. Using a Recurrent Neural Network (RNN), one can feed a model existing music and ask it to generate something new. By learning the patterns and rhythms of notes from a variety of compositions, the model should be able to output an original and melodical sequence.

Although these may not be the most amazing tracks in the world, they do serve a purpose. Music production platform Jukedeck allows users to input their requirements for a piece of music (genre, temp, mood, length, instruments etc.) that can then be automatically generated using AI. Obviously these aren’t designed to be chart hits, but production music that can be purchased cost-efficiently for YouTubers, Short Films and other backing-tracks.  

Despite the fact that this remains the most common use of AI in music, some artists are looking to push this even further. Musician Taryn Southern, for example, has created an EP based purely on AI compositions generated using Amper Score. The platform generated a beat, melody and basic structure before Southern then rearranged and added lyrics too. Could this form of collaboration become the future of mainstream music?

Rage Against the Machine Learning


As with any change, AI’s interruption of the music industry is not without controversy, and there are those who believe that the human contribution is what makes music what it is. 

Indeed, there are still several limitations to what AI can achieve creatively. Despite a neural network’s success with creating original compositions, another’s ability to write lyrics was somewhat lacklustre. Despite being trained on a combination of lyrics (for structure), and literature (for vocabulary), its output was largely nonsense and included lines such as “I got monk that wear you good”.  

Perhaps, like Southern’s compositions, AI is best used as an accompanying tool. London-based start-up AI Music offer technology that ‘shape-shifts’ songs to adapt to the context in which they’re played. This could be anything from tempo changes to match a listener’s speed to remastering tracks to appeal to different moods and situations. IBM’s Watson Beat, on the other hand, creates compositions that naturally fit to the visuals of a video. In this context, as within many other industries, AI looks set to support our existing skillsets rather than replace jobs. 

Whether you’re looking to create collaborative technologies or revolutionise an industry, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our specialist consultants to find out more.

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Is Computer Vision at the Core of the New Normal?

Computer Vision is one of the fastest growing markets in Data & Analytics. While it was on a trajectory prior to the pandemic, the needs we have now have amped up the role Computer Vision plays in our day-to-day lives and businesses who want to keep up or get ahead are paying attention.  Unexpected Businesses Using Computer Vision Some unusual players leaning on these technologies are grocery stores. While some have pivoted to pickup and delivery, others have remained stagnant with yesterday’s shopping habits changed only to individuals in store wearing masks. For those who made the leap to the "new normal", they’re using things like shelf sensors and Machine Learning to automate ordering and determine best placement of a product. Though retail stores are no stranger to video analytics, the rise of Deep Learning and AI offer a more rapid analysis of video for real-time threat assessment. Teaching the machine to watch for crowding, erratic movement, or potential conflict allows for quick reaction or proactive measures to stop a conflict in play. Yet, behind all this Machine Learning and Computer Vision elements are people. Real live humans. And it’s their new normal which is a strong part of the world’s new normal as most everyone shifts and remains online, working remotely. Behaviours are changing and many businesses have differentiated themselves from others by staying ahead of the game.        Five Ways Businesses Are Moving Forward in the New Normal Remote work is here to stay. A jump of 18% of remote working after the pandemic is expected to remain key to many businesses. And nearly three quarters of executives, plan to increase their remote workers. Key components of this new change will be to bring onboard those with strong digital collaboration skills, ability to manage virtually, and reassess how goals and objectives are to be decided. How will businesses keep remote employees engaged, enthused, and feel part of the team when they could be miles or countries apart?Gig Workers as Cost-Saving Measure. As employees move out of office and online, gig workers are a go-to for businesses hoping to move forward and keep costs low. Performance management systems will need to be re-evaluated. After all, if the idea is to keep costs low (read: overhead), then how does the debate about whether or not to offer benefits fit in to the mix?Definitions are Changing. Whether the definition includes ‘critical skills,’ ‘critical role,’ or something similar. What these meant once are changing. Now, the focus is on how to encourage, mentor, or coach employees in professional development skills which can open up a variety of opportunities versus one set path to one set role.Keeping Track Virtually. Though most businesses tend to follow the model of ‘productivity and performance’ over simply hours worked, some organisations passively track their remote workforce. This keeping track can include timeclock software virtually managed to computer usage to monitoring communications. Several benefits of data tracking in this manner could be a boon to HR Managers as it could help to understand employee engagement. But it’s a fine line to traverse.Organisational Redesign Done with Efficiency in Mind. As everything from products to people move online, it’s more important than ever to ensure things like logistics, supply chains, and workflows are designed with efficiency in mind. Computer Vision AI models can help take these systems to the next level as things like grocery shopping, retail, and legacy businesses find their business must go online or pivot in the new normal to survive. In our recently released 2020 Salary Guide we discuss each specialism. What’s working. What isn’t. And how businesses can hire and retain top talent to keep their projects on track and their businesses running smoothly.If you’re interested in Data & Technology, Risk or Digital Analytics, Life Sciences Analytics, Marketing & Insight, or Data Science, check out our current opportunities. Alternatively, you can contact one of our expert consultants if you’d like to learn more. 

The Search For Toilet Paper: A Q&A With The Data Society

We recently spoke Nisha Iyer, Head of Data Science, and Nupur Neti, a Data Scientist from Data Society.  Founded in 2014, Data Society consult and offer tailored Data Science training for businesses and organisations across the US. With an adaptable back-end model, they create training programs that are not only tailored when it comes to content, but also incorporate a company’s own Data to create real-life situations to work with.  However, recently they’ve been looking into another area: toilet paper.  Following mass, ill-informed, stock-piling as countries began to go into lockdown, toilet paper became one of a number of items that were suddenly unavailable. And, with a global pandemic declared, Data Society were one of a number of Data Science organisations who were looking to help anyway they could.  “When this Pandemic hit, we began thinking how could we help?” says Iyer. “There’s a lot of ways Data Scientists could get involved with this but our first thought was about how people were freaking out about toilet paper. That was the base of how we started, as kind of a joke. But then we realised we already had an app in place that could help.” The app in question began life as a project for the World Central Kitchen (WCK), a non-profit who help support communities after natural disasters occur.  With the need to go out and get nutritionally viable supplies upon arriving at a new location, WCK teams needed to know which local grocery stores had the most stock available.  “We were working with World Central Kitchen as a side project. What we built was an app that supposed to help locate resources during disasters. So we already had the base done.” The app in question allows the user to select their location and the products they are after. It then provides information on where you can get each item, and what their nutritional values are, with the aim of improving turnaround time for volunteers.  One of the original Data Scientists, Nupur Neti, explained how they built the platform: “We used a combination of R and Python to build the back-end processing and R Shiny to build the web application. We also included Google APIs that took your location and could find the closest store to you. Then, once you have the product and the sizes, we had an internal ranking algorithm which could rank the products selected based on optimisation, originally were based on nutritional value.”  The team figured that the same technology could help in the current situation, ranking based on stock levels rather than nutritional value. With an updated app, Iyer notes “People won’t have to go miles and stand in lines where they are not socially distancing. They’ll know to visit a local grocery store that does have what they need in stock, that they’ve probably not even thought of before.” However, creating an updated version presented its own challenges. Whereas the WCK app utilised static Data, this version has to rely on real-time Data. Unfortunately this isn’t as easy to come by, as Iyer knows too well:  “When we were building this for the nutrition app we reached out to groceries stores and got some responses for static Data. Now, we know there is real-time Data on stock levels because they’re scanning products in and out. Where is that inventory though? We don’t know.” After putting an article out asking for help finding live Data, crowdsourcing app OurStreets got in touch. They, like Data Society, were looking to help people find groceries in short supply. But, with a robust front and back-end in place, the app already live, and submissions flying in across the States, they were looking for a Data Science team who could make something of their findings.  “We have the opportunity,” says Iyer “to take the conceptual ideas behind our app and work with OurStreets robust framework to create a tool that could be used nationwide.” Before visiting a store, app users select what they are looking for. This allows them to check off what the store has against their expectations, as well as uploading a picture of what is available. They can also report on whether the store is effectively practising social distancing. Neti explains, that this Data holds lots of possibilities for their Data Science team: “Once we take their Data, our system will clean any submitted text using NLP and utilise image recognition on submitted pictures using Deep Learning. This quality Data, paired with the Social Distancing information, will allow us to gain better insights into how and what people are shopping for. We’ll then be able to look at trends, see what people are shopping for and where. Ultimately, it will also allow us to make recommendations as to where people should then go if they are looking for a product.”  In addition to crowdsourced information, Data Society are still keen to get their hands on any real-time Data that supermarkets have to offer. If you know where they could get their hands on it, you can get in touch with their team.  Outside of their current projects, Iyer remains optimistic for the world when it emerges from the current situation: “Things will return to normal. As dark a time as this is, I think it’s going to exemplify why people need to use Artificial Intelligence and Data Science more. If this type of app is publicised during the Coronavirus, maybe more people will understand the power of what Data and Data Science can do and more companies that are slow adaptors will see this and see how it could be helpful to their industry.”   If you want to make the world a better place using Data, we may have a role for you, including a number of remote opportunities. Or, if you’re looking to expand and build out your team with the best minds in Data, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

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