Resonate
Low-latency, high-resolution spectral analysis
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Last April I shared about my Resonate project here (https://news.ycombinator.com/item?id=43694157)A lot has happened since: the work I presented in much more detail at last June's International Computer Music Conference (ICMC) got best paper award. I also gave a talk at the Audio Developer Conference in Bristol last November, the video is on YouTube).This year's work, which I recently presented at this year's ICMC, starts with known techniques from the phase vocoder literature to build self-tuning filter banks that extract very efficiently the frequency components that are actually present in the input signal. Overview on the project website, more details in the papers, including applications to super-resolution spectrograms and re-synthesis experiments.As many people have pointed out, none of the techniques I have used are new (some of them even have different names across different fields), but I haven't seen them applied together in this way, and to me the results are incredibly satisfying and sometimes look magical. See for example this demo: https://youtu.be/LasdoIJJkw8Of course the best way to experience in person is through the free demo app: https://alexandrefrancois.org/OscillatorsLooking forward to feedback from the community!
HN · launched Jun 6, 2026 · 46 upvotes (top 17%) · 0 views
Niche: digital signal processing libraries · 32 launches, 1 in the last 7 days · Explore this niche →
Category and tags
- Horizontal
- Analytics & BI
- Prosumer
- Not AI
- Commercial product
Visit website ↗ Source listing ↗
More like this: all 32 launches in digital signal processing libraries →
Could you build this?
No Resonate is a novel, peer-reviewed mathematical algorithm for low-latency spectral analysis using custom phasor and EWMA formulations. It represents specialized digital signal processing (DSP) academic research rather than code an LLM can simply assemble.
What it would actually take: Building Resonate requires deep theoretical understanding of discrete-time signal processing, filterbank design, and the phase vocoder. The core algorithm is implemented in C/C++ or Rust to process audio samples in real time (e.g., 44.1/48 kHz sample-by-sample update loops) with minimal latency and SIMD optimizations. Validating mathematical stability, numerical precision, and perceptual accuracy across frequency spectrums requires specialized DSP engineering skills.
Discussion
20 comments analyzed.
Competitors mentioned: MelonAid (watermelon ripeness app), Chord Detector app, Resonance Chromatic Tuner app, Siren's Chord effect
Concerns raised: Lower frequency limit (32.7 Hz) misses piano fundamentals below that threshold, iPhone microphone hardware cannot reliably capture frequencies below 50Hz, Lowest piano keys don't show lowest frequency values as expected, Poor transient handling in synthesis results, Resonator bins drift slowly and collide with each other
Feature requests: Lower frequency detection limit (down to 17Hz for watermelon ripeness detection), Better transient detection and handling, Repulsive force to prevent resonator bin collision, Adaptive timbre modeling to factor out harmonics, Frequency component tracking output as (frequency, amplitude) pairs
Competitors
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Attention rank: #49 of 245 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 213 days after the earliest competitor.
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