I remember hearing about Bitcoin for the first time during an English lecture at Morehouse College in 2012. Not from the professor, but from one of my classmates. I had no clue how to get my hands on any and no clue how blockchain technology would evolve over roughly a decade. Around five years passed before I started working at Storj, a decentralized cloud storage startup with its own utility token on the Ethereum network. 2017 was an inflection point for crypto, where crypto companies attracted huge sums of money from all directions and we saw the launch of some of the first notable NFT projects like CryptoKitties and CryptoPunks. Right before my arrival, Storj raised 30 million dollars in an initial coin offering within a matter of days. Money that was completely non-dilutive, meaning that everyone kept the exact same equity stakes after the raise. Over the next two years at the company, I realized the power of crowdsourced networks that leverage modern technology and research.

Digital cash built on cryptographic primitives is powerful because it shifts the delegation of trust and execution of incentive models to code and mathematics instead of central parties. Over the recent years, we’ve seen blockchain technology begin to gain more momentum, building on top of the shoulders of researchers that formed the foundation of electronic cash (also referred to as digital cash) decades earlier. As the use of the internet became more widespread, researchers began to think about ways to create some of the same attractive features inherent in paper cash to the digital world. One of such features is being able to separate financial transactions from the identities of people. As we’ve seen with the Ethereum blockchain, these crypto primitives can be used to create architectures that track state changes beyond digital currency transactions. However, the momentum had to start somewhere. David Chaum, Amos Fiat, and Moni Naor published a research paper called “Untraceable Electronic Cash” that served as a source of inspiration for digital cash systems to come.

Introduction to Digital Signal Processing (DSP)

Digital Signal Processing (DSP) is an area of computer science that focuses on the study and use of operations on digital signals. Signals apply to speech, music, electric voltage, images, temperature, and more. My interest is primarily in audio signals related to music, but the fundamental concepts are all the same. Using the techniques formed from the field of DSP can help us do some pretty amazing things with signals. After giving a brief summary on a few ways that DSP applies to music, we’ll talk about the conversion process that allows continuous signals to be converted into digital signals vice versa.

DSP for Music

Digital Signal Processing for Music or Music Signal Processing has a lot of interesting applications. An interesting area of DSP for Music relates to the inner workings of digital audio workstations (DAWs) like Pro Tools, FL Studio, Logic, and Abelton. These software tools are driven by systems designed to apply effects such as delay, reverb, equalization, and distortion to audio signals. Your favorite songs make great use of these effects driven by DSP. Other interesting areas of DSP for music are sound recognition, audio fingerprinting, sound synthesis, music generation, audio source separation, and more. As you see with a few of the aforementioned topics, DSP can be used in tandem with other fields of study like artificial intelligence.

“If I can make a teacher’s salary doing comedy, I think that’s better than being a teacher.” - Dave Chappelle

I have to be honest with you all. I just want a place to post my unadulterated thoughts on topics that I like to talk about. Another intent is to write about topics that I believe may be helpful to others. Plenty of blogs talk specifically about one topic consistently and do it very well. This isn’t one of those blogs. I have a handful of topics that I’m interested in, so I’ll talk about all of them. I’ll use tags to help you all get to the stuff that you care about.