Audio Signal Processing for Machine Learning | Highlights and Annotations by Gistr.

This YouTube video series will cover audio digital signal processing (DSP) for machine learning. It addresses the lack of resources on audio data preprocessing for deep learning applications. The series will cover DSP fundamentals, audio features (e.g., spectrograms), transformations (Fourier, STFT, etc.), audio perception, and practical implementation using Python and Librosa. The target audience includes machine learning engineers, computer science students, software engineers interested in audio, and music technologists. The series will blend theoretical explanations with coding examples available on GitHub. Okay, so now a question is, are so where do we use audio, digital signal processing for machine learning and for deep learning specifically? Well, there are a bunch vacations in audio where reaches audio signal processing. So obviously you have all sorts of audio classification of problems, then speech recognition, speaker verification, speaker diaries, Asian for example and then all your denoising audio sampling. And if you are a music type of guy, there's a whole field for you that's called music information retrieval that's uses our tools from digital signal processing along with machine learning to crack certain problems like music instruments, identification or music, mood and genre classification. And there's a bunch bunch more of those. Cool. Okay, so what are we gonna cover in this series? So it's a lot of stuff really and it's not sats on the stone yet. So y'all I am open to you to get feedback from you guys on like what topics like to cover during the the process like of this a serious but for sure i'm gonna cover some waves digital to analog converters analog to digital converters and then I'll jump in see all your features and we'll take a look at time and frequency domain or your features like our a mass spectrum centroid an FCC's Then we're going to also look at a bunch of very important audio transformations we'll take a look at the Fourier transform the short time Fourier transform that leads to spectrograms then go compare that against other transformations like the constant Q transform the male spectrograms and chroma Graham's on top of that we're going to also take a look at topics in audio and music perception which we can leverage to pre--process the audio data in a way that makes sense for the current problem that we're trying to solve. Okay, so what should you expect from this series if you usually follow the sound of a our channel, you know that I laughed cover both if your ethical stuff and implementation stuff.