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      <header><h2 class="page__title" itemprop="headline">Code</h2></header>
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        <p>Source code for some of the projects I have been involved with are found below</p>
        <ul>
          <li><a href="https://github.com/joshreiss/Working-with-the-Web-Audio-API">Working with the Web Audio API</a></li>
          <li><a href='https://github.com/joshreiss/Meta-analysis-of-high-res-audio-perception'>Meta-analysis of hi-res audio</a> - Additional information, source code, data and data analysis for the paper "Meta-analysis of high resolution audio perceptual evaluation"</li>
          <li><a href="https://github.com/joshreiss/All-about-audio-EQ">All about EQ</a> - Supplemental materials accompanying the paper Vesa Välimäki and Joshua D. Reiss, "All About Audio Equalization: Solutions and Frontiers," Applied Sciences, vol. 6, 2016.</li>
          <li><a href="https://diffapf.github.io/web">Differentiable All-pole Filters for Time-varying Audio Systems</a></li>
          <li><a href="https://christhetr.ee/mod_extraction/">Modulation extraction</a></li>
          <li><a href="https://christhetr.ee/mod_discovery/">Modulation discovery</a></li>
          <li><a href="https://github.com/sai-soum/Diff-MST">Diff-MST</a> - Differentiable Mixing Style Transfer</li>
          <li><a href="https://github.com/adobe-research/DeepAFx-ST">DeepAFx-ST</a> - Style transfer of audio effects with differentiable signal processing. Please see <a href="https://csteinmetz1.github.io/DeepAFx-ST/"</a>https://csteinmetz1.github.io/DeepAFx-ST/</li>
          <li><a href="https://github.com/csteinmetz1/micro-tcn">Efficient neural networks for analog audio effect modeling</li>
          <li><a href="https://github.com/csteinmetz1/IIRNet">IIRNet</a> - Direct design of biquad filter cascades with deep learning by sampling random polynomials. See <a href="csteinmetz1.github.io/IIRNet/">csteinmetz1.github.io/IIRNet/</a></li>
          <li><a href="https://github.com/csteinmetz1/steerable-nafx">Steerable-NAFX</a> - Steerable discovery of neural audio effects. See <a href="">csteinmetz1.github.io/steerable-nafx</a></li>
          <li><a href="https://github.com/BrechtDeMan/WebAudioEvaluationTool">The Web Audio Evaluation Tool</a> - A tool based on the HTML5 Web Audio API to perform perceptual audio evaluation tests locally or on remote machines over the web.</li>
        </ul>
        <p>Data sets</p>
        <ul>
          <li><a href="https://github.com/nellyngz95/6KSFX">6KSFx</a>- Procedural Audio Dataset, providing 6,000 synthetic audio samples</li>
          <li><a href="https://ispamm.github.io/FolAI/wtm.html">Walking the maps</a> - includes short videos from gameplay sessions of 4 video games: Hogwarts Legacy, Zelda Breath of the Wild, Assassin's Creed: Odyssey, Assassin's Creed IV Black Flag. For each video, we extracted only clips in which the sound of the steps is clearly audible and no other sound sources related to the target sound.</li>
          <li><a href="https://mcomunita.notion.site/Audio-Effects-Research-bf166505e77e494fb6d11a0833e0a842">AFxResearch</a>- a repository and website of audio effects research</li>
          <li>SynthFx 12K Dataset - contains 12,000 five-second sound samples, across 30 categories of real and synthetic audio. See <a href = "documents/2024/Garcia - Chime - SynthFX.pdf">this short paper</a></li>
          <li><a href="documents/2017/De Man DAFx17_paper_49.pdf">The mix evaluation dataset</a> - audio mixes gathered in a real-life setting, and perceptual evaluation thereof. With 180 mixes including parameter settings, close to
            5000 preference ratings and free-form descriptions, and a diverse range of contributors from five different countries 2017.</li>
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