Machine learning for music: Google’s Tone Transfer
Google made a low-key launch this week, when it made Tone Transfer public. Built by two teams at Mountain View – Magenta and AI UX – the tool takes a tonal input (a voice or a line of melody) and can then re-render it with instrument modelling.
The year-long collaboration between AI researchers, UX engineers and designers is built on Magenta’s Differential Digital Signal Processing engine (DDSP). It was created as an exercise in learning about how people perceive music, machine learning and their own practice. Running on an early version of DDSP, it’s an in-browser deployment (on tensorflow.js) which extracts pitch data using another Google Research project, SPICE .
At the moment, the tool has been opened up for experiment by musicians and non-musicians who want to explore music creation. But Magenta made DDSP open source earlier this year and, while it hasn’t been expressly stated, there may be implications for business data collection.
As new routes to mining data are explored, and voice collection via VOIP services becomes more commonplace, there is scope to explore tonal approaches to voice data. Applying machine learning to voice data could help to parse language that otherwise could be misinterpreted by data analysts. Sarcasm or humour are both common idioms that would create a false positive if voice data is mined in like-for-like fashion with text.
Magenta says: “We are excited with upcoming releases enabling you to easily train your own DDSP models and deploy them everywhere: a phone, an audio plugin or a website using the larger tensorflow lite and tensorflow.js ecosystem.”
Data scientists, always on the lookout for a new frontier, should prick their ears.
HPE Acquires Determined AI to Accelerate ML Training
Determined AI is a four-year-old company, which only brought its product to market in 2020. It specialises in machine learning (ML), with the aim of training AI models quickly and at any scale. HPE will combine Determined AI’s unique software solution with its AI and high-performance computing (HPC) offerings to enable ML engineers to easily implement and train ML models to provide faster and more accurate insights from their data in almost every industry.
“As we enter the Age of Insight, our customers recognise the need to add machine learning to deliver better and faster answers from their data,” said Justin Hotard, senior vice president and general manager, HPC and Mission Critical Solutions (MCS), HPE. “AI-powered technologies will play an increasingly critical role in turning data into readily available, actionable information to fuel this new era. Determined AI’s unique open source platform allows ML engineers to build models faster and deliver business value sooner without having to worry about the underlying infrastructure. I am pleased to welcome the world-class Determined AI team, who share our vision to make AI more accessible for our customers and users, into the HPE family.”
Delivery AI at scale
According to IDC, the accelerated AI server market, which plays an important role in providing targeted capabilities for image and data-intensive training, is expected to grow by 28% each year and reach $18bn by 2024.
The computing power of HPC is also increasingly being used to train and optimise AI models, in addition to combining with AI to augment workloads such as modeling and simulation. Intersect360 Research notes that the HPC market will grow by more than 40%, reaching almost $55bn in revenue by 2024.
“Over the last several years, building AI applications has become extremely compute, data, and communication intensive. By combining with HPE’s industry-leading HPC and AI solutions, we can accelerate our mission to build cutting edge AI applications and significantly expand our customer reach.” said Evan Sparks, CEO of Determined AI.