# signal processing Published articles for signal processing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Introducing Voice Boost 2: Remaster your podcasts DevFeed: [Introducing Voice Boost 2: Remaster your podcasts]() Original publisher: [Read original article]() Author: Marco Arment Published: 2020-01-31T18:46:32Z Content type: release Language: en Sources: [Marco.org]() Topics: [Compression](), [C](), [Streaming](), [Code]() Tags: [c](), [compression](), [signal-processing](), [streaming](), [update]() ### AI overview Overcast's Voice Boost 2 is a new audio engine for podcast loudness normalization. It analyzes incoming audio and applies more subtle, adaptive processing than the original Voice Boost, aiming for more consistent volume and improved sound quality while operating as a streaming process. ### Source excerpt The latest update to Overcast includes a feature that I'm especially proud of that took over a year to build. Voice Boost 2 is an all-new audio engine that includes professional-grade, mastering-quality loudness normalization. When I first introduced Overcast in 2014, Voice Boost was one of its headlining features: Voice Boost is a combination of dynamic compression and equalization that can make many shows more listenable and normalize volume across all shows. This makes amateur-produced podcasts (including many of my favorites) more listenable in loud environments, like cars, where you'd otherwise need to crank the volume so loudly to hear the quiet parts that you'd blow your ears out when the loudest person spoke. Voice Boost 2 achieves the same goal as the original Voice Boost, but with dramatically more sophisticated methods, leading to more consistent results and much better sound quality. Goals When I wrote the original Voice Boost with only a rudimentary understanding of audio processing, it was a single configuration of Apple's AudioUnits that applied a fixed set of parameters to all podcasts, regardless of their audio characteristics. It was an effective but blunt tool, relying on aggressive level compression and a strong EQ to make the compression less noticeable. Since then, I've edited over 500 podcasts, learned a lot more about how to master them properly, and developed a much better understanding of audio signal processing. I set out to develop a better, smarter, and more refined Voice Boost that took advantage of everything I'd learned, with these audio goals: The effect should be much more subtle: mostly just consistent volume, plus slightly smoother tone. It should analyze the input audio and apply just enough processing to achieve a consistent volume level, modifying already-good audio as little as possible. It should sound good, and consistent, regardless of the volume dynamics of its input. Quality should be so good that I can even play high-qua ## Making Hybrid Images DevFeed: [Making Hybrid Images]() Original publisher: [Read original article]() Published: 2014-09-29T10:00:06Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Mathematics](), [Computing](), [Programming](), [Code]() Tags: [albert-einstein](), [art](), [dali](), [design](), [fourier-analysis](), [hybrid-images](), [image-manipulation](), [images](), [marilyn-monroe](), [mathematics](), [mona-lisa](), [programming](), [python](), [salvador-dali](), [signal-processing]() ### AI overview This article explains hybrid images, a visual technique in which different images appear at different viewing distances. It connects examples such as the Mona Lisa, Salvador Dali's work, and the Marilyn Einstein image to Fourier analysis and the use of mathematics, science, and programming. ### Source excerpt The Mona Lisa Leonardo da Vinci's Mona Lisa is one of the most famous paintings of all time. And there has always been a discussion around her enigmatic smile. He used a trademark Renaissance technique called sfumato, which involves many thin layers of glaze mixed with subtle pigments. The striking result is that when you look directly at Mona Lisa's smile, it seems to disappear. But when you look at the background your peripherals see a smiling face. ## The Fast Fourier Transform DevFeed: [The Fast Fourier Transform]() Original publisher: [Read original article]() Published: 2012-07-18T08:00:54Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithm](), [processing]() Tags: [algorithm](), [analysis](), [audio](), [divide-and-conquer](), [fourier-analysis](), [fourier-transform](), [graphics](), [mathematics](), [processing](), [programming](), [python](), [signal-processing](), [sound]() ### AI overview A tutorial on the Fast Fourier Transform explains its historical development, the improvement from O(n^2) to O(n log n) computation for the discrete Fourier transform, and a derivation and implementation approach. It also explores audio denoising by filtering a noisy signal's frequency spectrum. ### Source excerpt It's often said that the Age of Information began on August 17, 1964 with the publication of Cooley and Tukey's paper, "An Algorithm for the Machine Calculation of Complex Fourier Series." They published a landmark algorithm which has since been called the Fast Fourier Transform algorithm, and has spawned countless variations. Specifically, it improved the best known computational bound on the discrete Fourier transform from $ O(n^2)$ to $ O(n \log n)$, which is the difference between uselessness and panacea.