About Me

Hi all,

My name is Ziwen Ren (Kevin). I am a music technologist, music producer, and jazz pianist. I am currently focused on audio programming, DSP, and machine learning. During my college years, I first majored in Jazz Composition and then transferred to the Electronic Production and Design (EPD) major.

I dove deep into DSP and invented an additive jazz chord synthesizer using Max/MSP. I also programmed a convolution reverb in C++ using JUCE and coded a Schroeder reverb using Max/MSP. I also taught myself calculus to understand DSP algorithms. During my final year at Berklee, I developed an AI model for guitar effect reconstruction. After several iterations, I used DDSP as the foundation to train a model for reconstructing delay-line-based effects.

Besides being a technologist, I also composed and performed a lot during my first two years at Berklee. My professors and classmates were sometimes surprised that I composed both EDM and jazz music. I studied jazz piano performance in small ensembles and composition for big bands, learning and researching jazz theory from the 1990s to modern jazz. I also studied sound synthesis, mixing techniques, and EDM composition during my time in the EPD major.

I was born in Wenzhou, China, a vibrant city full of energy and passion. Since elementary school, I have always had very uneven strengths across different subjects. I was relatively weak in humanities and language subjects, but I was strong in mathematics and science. Within China’s exam-oriented education system, while most students tried to answer questions using the expected methods, I preferred to take a different path and find alternative ways to solve mathematical problems.

Searching for the underlying logic of a mathematical problem usually took a great deal of time, and it rarely benefited me on exams. This made it difficult for me to adapt to the Chinese education system, and it also meant that my overall scores were never high enough for admission to a highly selective university. When I felt lost and did not know where my future would lead, I thought about my experience of studying classical piano since childhood and decided to go to Berklee College of Music in the United States to study music composition.

At first, I was deeply passionate about jazz. I was amazed by the complexity of jazz harmony. Through jazz, I discovered the natural beauty of music as a language, as well as its powerful ability to drive social progress. Musicians can solo over the same set of chord changes and tell completely different stories. This made me realize that music is, in essence, a story and a language.

During my first year at Berklee, I participated in many performances and met many interesting musicians. Driven by curiosity, I was not satisfied with performance alone. I wanted to explore the underlying logic of music, so I transferred to Electronic Production and Design and focused on DSP.

I came to understand that music production is not only about the original sound itself but also about the art of mixing. With the help of modern signal processing theory, the underlying logic of mixing a piece of audio can be extremely complex. Effects that produce similar results may be implemented in completely different ways. I became fascinated by DSP and ultimately chose to explore the possibilities between machine learning and DSP.

In my future career, I hope to continue exploring the fields related to music and machine learning. I want to help advance the field of audio machine learning and make the benefits of ML accessible to every music producer.