In this podcast episode, I was lucky to interview another member of the Aalto Acoustics Lab of the Aalto University in Espoo, Finland. He is involved in research applying deep learning to audio, for example, in the context of Virtual Analog modeling of guitar amplifiers. He was also the advisor of my Master Thesis. In this podcast episode, you will learn ✅ how to transition from mechanical engineering to audio processing, ✅ how to apply deep learning to music processing, for example, virtual analog modeling, ✅ challenges of deep learning in audio, ✅ how to use neural networks in real-time audio, ✅ what makes a good audio dataset, ✅ how to fuse traditional DSP and deep learning, ✅ typical neural network architectures in audio, ✅ generative models in audio, ✅ how transition from the Master's to the PhD mindset, ✅ challenges of remote research collaboration during pandemic, ✅ transition from Scotland to Finland. You can find all referenced resources at https://www.thewolfsound.com/talk004/ 📝 Note: if you like the podcast so far, please, go to iTunes and leave me a review there. It will benefit both sides: more reviews means more outreach on iTunes and feedback can help me to improve the show and provide better quality content to you. Thank you for doing this 🙏 All podcast episodes: https://www.thewolfsound.com/podcast/ Buy a coffee if you want to support this podcast: https://www.buymeacoffee.com/janwilczek Sign up for the WolfSound newsletter to master audio programming: https://thewolfsound.com/newsletter/

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.

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