With machine learning, we can train a deep neural network to remove all varieties of rustle we can record, even though we may not understand how exactly itРІР‚в„ўs doing it. This made developing an algorithm to remove rustle in all its variations nearly impossible. However, a problem like lav-mic rustle can vary from high-frequency РІР‚СљcracklingРІР‚Сњ to low-frequency thuds or bumps, and change in unpredictable ways based on how a person moves or the type of clothes they wear. The current suite of audio repair tools in RX work great for well-defined disturbances, like clicks (short in time) or plosives (always in low frequencies). At iZotope some of our previous machine learning applications were to automatically identify instruments as part of the Neutron Track Assistant feature, and to automatically detect song structure (i.e., verse/chorus) for improved waveform navigation in the Ozone standalone application. Machine learning is also starting to allow for innovations in the audio industry. Some of the most familiar applications of machine learning are speech recognition (Siri, Amazon Alexa), where algorithms transcribe the words we speak, or image recognition where algorithms automatically label items, places, and faces in images (think of how Facebook accurately suggests people to tag in uploaded photos, and how your email can automatically recognize spam).

Machine learning refers to a class of algorithms that discover patterns in data and use those discovered patterns to make predictions when presented with new data.

You may have heard of machine learning but not exactly understood what the term means, or how we use it in our everyday lives.

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