Creative Buffer

creativebuffer

Moving to the upcoming WebGPU means more than just switching graphics APIs. It's also a step towards the future of web graphics. But this migration will turn out better with preparation and understanding — and this article will get you ready. Hello everyone, my name is Dmitrii Ivashchenko and I'm a software engineer at MY.GAMES. In this article, we'll discuss the differences between WebGL and the upcoming WebGPU, and we'll lay out how to prepare your project for migration. Content Overview Timeline of WebGL and WebGPU The current state of WebGPU, and what's to come High-level Conceptual Differences Initialization • WebGL: The Context Model • WebGPU: The Device Model Programs and Pipelines • WebGL: Program • WebGPU: Pipeline Uniforms • Uniforms in WebGL 1 • Uniforms in WebGL 2 • Uniforms in WebGPU Shaders • Shader Language: GLSL vs WGSL • Comparison of Data Types • Structures • Function Declarations • Built-in functions • Shader Conversion Convention Differences Textures • Viewport Space • Clip Spaces WebGPU Tips & Tricks • Minimize the number of pipelines you use. • Create pipelines in advance • Use RenderBundles Summary Timeline of WebGL and WebGPU , like many other web technologies, has roots that stretch back quite far into the past. To understand the dynamics and motivation behind the move towards WebGPU, it's helpful to first take a quick look at the history of WebGL development: WebGL The desktop version of OpenGL debuts. OpenGL desktop (1993) : This was the first stable release of WebGL, based on OpenGL ES 2.0, which was itself introduced in 2007. It provided web developers with the ability to use 3D graphics directly in browsers, without the need for additional plugins. WebGL 1.0 (2011) : Introduced six years after the first version, WebGL 2.0 was based on OpenGL ES 3.0 (2012). This version brought with it a number of improvements and new capabilities, making 3D graphics on the web even more powerful. WebGL 2.0 (2017) In recent years, there has been a surge of interest in new graphics APIs that provide developers with more control and flexibility: : Created by the Khronos group, this cross-platform API is the "successor" to OpenGL. Vulkan provides lower-level access to graphics hardware resources, allowing for high-performance applications with better control over graphics hardware. Vulkan (2016) : This API was created by Microsoft and is exclusively for Windows and Xbox. D3D12 is the successor to D3D10/11 and provides developers with deeper control over graphics resources. D3D12 (2015) : Created by Apple, Metal is an exclusive API for Apple devices. It was designed with maximum performance on Apple hardware in mind. Metal (2014) The current state of WebGPU, and what's to come Today, WebGPU is available on multiple platforms such as Windows, Mac, and ChromeOS through the Google Chrome and Microsoft Edge browsers, starting with version 113. Support for Linux and Android is expected in the near future. Here are some of the engines that already support (or offer experimental support) for WebGPU: : Full support for WebGPU. Babylon JS : Experimental support at the moment. ThreeJS : In development, but with very promising prospects. PlayCanvas : Very early and experimental WebGPU support was announced in version 2023.2 alpha. Unity : Officially supports WebGPU, making it one of the pioneers in this area. Cocos Creator 3.6.2 : currently supported in v113+ for Windows, macOS, and ChromeOS only. Construct Considering this, transitioning to WebGPU or at least preparing projects for such a transition appears to be a timely step in the near future. High-level Conceptual Differences Let's zoom out and take a look at some of the high-level conceptual differences between WebGL and WebGPU, starting with initialization. Initialization When starting to work with graphics APIs, one of the first steps is to initialize the main object for interaction. This process differs between WebGL and WebGPU, with some peculiarities for both systems. WebGL: The Context Model In WebGL, this object is known as "context", which essentially represents an interface for drawing on an HTML5 canvas element. Obtaining this context is quite simple: const gl = canvas.getContext('webgl'); The context of WebGL is actually tied to a specific canvas. This means that if you need to render on multiple canvases, you will need multiple contexts. WebGPU: The Device Model WebGPU introduces a new concept called "device". This device represents a GPU abstraction that you will interact with. The initialization process is a bit more complex than in WebGL, but it provides more flexibility: const adapter = await navigator.gpu.requestAdapter();
const device = await adapter.requestDevice();

const context = canvas.getContext('webgpu');
context.configure({
   device,
   format: 'bgra8unorm',
}); One of the advantages of this model is that one device can render on multiple canvases or even none. This provides additional flexibility; for example, one device may control rendering in multiple windows or contexts. Programs and Pipelines WebGL and WebGPU represent different approaches to managing and organizing the graphics pipeline. WebGL: Program In WebGL, the main focus is on the shader program. The program combines vertex and fragment shaders, defining how vertices should be transformed and how each pixel should be colored. const program = gl.createProgram();
gl.attachShader(program, vertShader);
gl.attachShader(program, fragShader);
gl.bindAttribLocation(program, 'position', 0);
gl.linkProgram(program); Steps for creating a program in WebGL: : The source code for shaders is written and compiled. Creating Shaders : Compiled shaders are attached to the program and then linked. Creating Program : The program is activated before rendering. Using Program : Data is transmitted to the activated program. Data Transmission This process allows for flexible graphics control, but can also be complex and prone to errors, especially for large and complex projects. WebGPU: Pipeline WebGPU introduces the concept of a "pipeline" instead of a separate program. This pipeline combines not only shaders but also other information, which in WebGL, is established as states. So, creating a pipeline in WebGPU looks more complex: const pipeline = device.createRenderPipeline({
 layout: 'auto',
 vertex: {
   module: shaderModule, entryPoint: 'vertexMain',
   buffers: [{
     arrayStride: 12,
     attributes: [{
       shaderLocation: 0, offset: 0, format: 'float32x3'
     }]
   }],
 },
 fragment: {
   module: shaderModule, entryPoint: 'fragmentMain',
   targets: [{ format, }],
 },
}); Steps to create a pipeline in WebGPU: : The shader source code is written and compiled, similar to how it's done in WebGL. Shader definition : Shaders and other rendering parameters are combined into a pipeline. Pipeline creation : The pipeline is activated before rendering. Pipeline usage While WebGL separates each aspect of rendering, WebGPU tries to encapsulate more aspects into a single object, making the system more modular and flexible. Instead of separately managing shaders and rendering states, as is done in WebGL, WebGPU combines everything into one pipeline object. This makes the process more predictable and less prone to errors: Uniforms Uniform variables provide constant data that is available to all shader instances. Uniforms in WebGL 1 In basic WebGL, we have the ability to set variables directly through API calls. uniform : GLSL uniform vec3 u_LightPos;
uniform vec3 u_LightDir;
uniform vec3 u_LightColor; : JavaScript const location = gl.getUniformLocation(p, "u_LightPos");
gl.uniform3fv(location, [100, 300, 500]); This method is simple, but requires multiple API calls for each variable. uniform Uniforms in WebGL 2 With the arrival of WebGL 2, we now have the ability to group variables into buffers. Although you can still use separate uniform shaders, a better option is to group different uniforms into a larger structure using uniform buffers. Then you send all this uniform data to the GPU at once, similar to how you can load a vertex buffer in WebGL 1. This has several performance advantages, such as reducing API calls and being closer to how modern GPUs work. uniform : GLSL layout(std140) uniform ub_Params {
   vec4 u_LightPos;
   vec4 u_LightDir;
   vec4 u_LightColor;
}; : JavaScript gl.bindBufferBase(gl.UNIFORM_BUFFER, 1, gl.createBuffer()); To bind subsets of a large uniform buffer in WebGL 2, you can use a special API call known as . In WebGPU, there is something similar called dynamic uniform buffer offsets where you can pass a list of offsets when calling the API. bindBufferRange setBindGroup Uniforms in WebGPU WebGPU offers us an even better method. In this context, individual variables are no longer supported, and work is done exclusively through buffers. uniform uniform : WGSL [[block]] struct Params {
   u_LightPos : vec4<f32>;
   u_LightColor : vec4<f32>;
   u_LightDirection : vec4<f32>;
};
[[group(0), binding(0)]] var<uniform> ub_Params : Params; : JavaScript const buffer = device.createBuffer({
  usage: GPUBufferUsage.UNIFORM,
  size: 8
}); Modern GPUs prefer data to be loaded in one large block, rather than many small ones. Instead of recreating and rebinding small buffers each time, consider creating one large buffer and using different parts of it for different draw calls. This approach can significantly increase performance. WebGL is more imperative, resetting global state with each call, and striving to be as simple as possible. WebGPU, on the other hand, aims to be more object-oriented and focused on resource reuse, which leads to efficiency. Transitioning from WebGL to WebGPU may seem difficult due to differences in methods. However, starting with a transition to WebGL 2 as an intermediate step can simplify your life. Shaders Migrating from WebGL to WebGPU requires changes not only in the API, but also in shaders. The WGSL specification is designed to make this transition smooth and intuitive, while maintaining efficiency and performance for modern GPUs. Shader Language: GLSL vs WGSL WGSL is designed to be a bridge between WebGPU and native graphics APIs. Compared to GLSL, WGSL looks a bit more verbose, but the structure remains familiar. Here's an example shader for texture: : GLSL sampler2D myTexture;
varying vec2 vTexCoord;
void main() {
  return texture(myTexture, vTexCoord);
} : WGSL [[group(0), binding(0)]] var mySampler: sampler;
[[group(0), binding(1)]] var myTexture: texture_2d<f32>;
[[stage(fragment)]]
fn main([[location(0)]] vTexCoord: vec2<f32>) -> [[location(0)]] vec4<f32> {
  return textureSample(myTexture, mySampler, vTexCoord);
} Comparison of Data Types The table below shows a comparison of the basic and matrix data types in GLSL and WGSL: Transitioning from GLSL to WGSL demonstrates a desire for stricter typing and explicit definition of data sizes, which can improve code readability and reduce the likelihood of errors. Structures The syntax for declaring structures has also changed: GLSL: struct Light {
  vec3 position;
  vec4 color;
  float attenuation;
  vec3 direction;
  float innerAngle;
  float angle;
  float range;
}; WGSL: struct Light {
  position: vec3<f32>,
  color: vec4<f32>,
  attenuation: f32,
  direction: vec3<f32>,
  innerAngle: f32,
  angle: f32,
  range: f32,
}; Introducing explicit syntax for declaring fields in WGSL structures emphasizes the desire for greater clarity and simplifies understanding of data structures in shaders. Function Declarations : GLSL float saturate(float x) {
	return clamp(x, 0.0, 1.0);
} : WGSL fn saturate(x: f32) -> f32 {
  return clamp(x, 0.0, 1.0);
} Changing the syntax of functions in WGSL reflects the unification of the approach to declarations and return values, making the code more consistent and predictable. Built-in functions In WGSL, many built-in GLSL functions have been renamed or replaced. For example: Renaming built-in functions in WGSL not only simplifies their names, but also makes them more intuitive, which can facilitate the transition process for developers familiar with other graphics APIs. Shader Conversion For those who are planning to convert their projects from WebGL to WebGPU, it's important to know that there are tools for automatically converting GLSL to WGSL, such as **[Naga](https://github.com/gfx-rs/naga/)**, which is a Rust library for converting GLSL to WGSL. It can even work right in your browser with the help of WebAssembly. Here are Naga supported end-points: Convention Differences Textures After migration, you may encounter a surprise in the form of flipped images. Those who have ever ported applications from OpenGL to Direct3D (or vice versa) have already faced this classic problem. In the context of OpenGL and WebGL, textures are usually loaded in such a way that the starting pixel corresponds to the bottom left corner. However, in practice, many developers load images starting from the top left corner, which leads to the flipped image error. Nevertheless, this error can be compensated by other factors, ultimately leveling out the problem. Unlike OpenGL, systems such as Direct3D and Metal traditionally use the upper-left corner as the starting point for textures. Considering that this approach seems to be the most intuitive for many developers, the creators of WebGPU decided to follow this practice. Viewport Space If your WebGL code selects pixels from the frame buffer, be prepared for the fact that WebGPU uses a different coordinate system. You may need to apply a simple "y = 1.0 - y" operation to correct the coordinates. Clip Spaces When a developer faces a problem where objects are clipped or disappear earlier than expected, this is often related to differences in the depth domain. There's a difference between WebGL and WebGPU in how they define the depth range of the clip space. While WebGL uses a range from -1 to 1, WebGPU uses a range from 0 to 1, similar to other graphics APIs such as Direct3D, Metal, and Vulkan. This decision was made due to several advantages of using a range from 0 to 1 that were identified while working with other graphics APIs. The main responsibility for transforming your model's positions into clip space lies with the projection matrix. The simplest way to adapt your code is to ensure that your projection matrix outputs results in the range of 0 to 1. For those using libraries such as gl-matrix, there is a simple solution: instead of using the function, you can use ; similar functions are available for other matrix operations. perspective perspectiveZO if (webGPU) {
	// Creates a matrix for a symetric perspective-view frustum
  // using left-handed coordinates
  mat4.perspectiveZO(out, Math.PI / 4, ...);
} else {
  // Creates a matrix for a symetric perspective-view frustum
  // based on the default handedness and default near
  // and far clip planes definition.
  mat4.perspective(out, Math.PI / 4, …);
} However, sometimes you may have an existing projection matrix and you can't change its source. In this case, to transform it into a range from 0 to 1, you can pre-multiply your projection matrix by another matrix that corrects the depth range. WebGPU Tips & Tricks Now, let's discuss some tips and tricks for working with WebGPU. Minimize the number of pipelines you use. The more pipelines you use, the more state switching you have, and the less performance; this may not be trivial, depending on where your assets come from. Create pipelines in advance Creating a pipeline and using it immediately may work, but this is not recommended. Instead, create functions that return immediately and start working on a different thread. When you use the pipeline, the execution queue needs to wait for pending pipeline creations to finish. This can result in significant performance issues. To avoid this, make sure to leave some time between creating the pipeline and first using it. Or, even better, use the variants! The promise resolves when the pipeline is ready to use, without any stalling. create*PipelineAsync device.createComputePipelineAsync({
 compute: {
   module: shaderModule,
   entryPoint: 'computeMain'
 }
}).then((pipeline) => {
  const commandEncoder = device.createCommandEncoder();
  const passEncoder = commandEncoder.beginComputePass();
  passEncoder.setPipeline(pipeline);
  passEncoder.setBindGroup(0, bindGroup);
  passEncoder.dispatchWorkgroups(128);
  passEncoder.end();
  device.queue.submit([commandEncoder.finish()]);
}); Use RenderBundles Render bundles are pre-recorded, partial, reusable render passes. They can contain most rendering commands (except for things like setting the viewport) and can be "replayed" as part of an actual render pass later on. const renderPass = encoder.beginRenderPass(descriptor);

renderPass.setPipeline(renderPipeline);
renderPass.draw(3);

renderPass.executeBundles([renderBundle]);

renderPass.setPipeline(renderPipeline);
renderPass.draw(3);

renderPass.end(); Render bundles can be executed alongside regular render pass commands. The render pass state is reset to defaults before and after every bundle execution. This is primarily done to reduce the JavaScript overhead of drawing. GPU performance remains the same regardless of the approach. Summary Transitioning to WebGPU means more than just switching graphics APIs. It's also a step towards the future of web graphics, combining successful features and practices from various graphics APIs. This migration requires a thorough understanding of technical and philosophical changes, but the benefits are significant. Useful Resources & Links: WebGPU — All of the cores, none of the canvas From WebGL to WebGPU in Construct by Alain Galvan Raw WebGPU tutorial by Brandon Jones WebGPU Best Practices WebGL + WebGPU Meetup - July 2023 Link

Migrating from WebGL to WebGPU

WebGL থেকে WebGPU-তে স্থানান্তরিত হচ্ছে

Migration von WebGL zu WebGPU

Migrando de WebGL a WebGPU

Migration de WebGL vers WebGPU

WebGL से WebGPU पर माइग्रेट किया जा रहा है

WebGL から WebGPU への移行

WebGL에서 WebGPU로 마이그레이션

Migrando de WebGL para WebGPU

Переход с WebGL на WebGPU

WebGL'den WebGPU'ya geçiş

Di chuyển từ WebGL sang WebGPU

从 WebGL 迁移到 WebGPU

This post might not fall into the "normal" ways that you'd use live streaming with Amazon Interactive Video Service (Amazon IVS), but it's a fun project to play around and learn with and it illustrates some unique features of the Web Broadcast SDK (notably the ability to stream from a source other than a traditional camera and microphone). You may be familiar with the concept of "lofi radio" channels, but if not, they usually consist of some sort of animated character in a mostly static scene with a looped audio track containing lofi music. These live streams are popular among people looking for a non-distracting soundtrack to relax, study, or chat and make new friends. It doesn't fit the "traditional" user generated content (UGC) model of a broadcaster streaming a game or their webcam, but there is no arguing that these streams are hugely popular. For example, as of the time of publishing this article, the channel on YouTube currently has 11.7 million subscribers and boasts nearly 1.5 total views. Lofi Girl billion Maybe it's time that someone built an entire live streaming UGC platform that gives users the ability to create their own custom lofi channels and broadcast them to their friends? That's a free idea - run with it! I'm going to assume that you're new to Amazon IVS, so we'll walk through the process of creating your own lofi live stream from the beginning. If you're already familiar with Amazon IVS, feel free to jump past the parts that you're already comfortable with. Sign Up for a Free AWS Account Step one is to . New accounts will be eligible for 5 hours of basic input, 100 hours of SD output, and a generous amount of monthly chat messages for the first 12 months. This should be plenty of time to play around with Amazon IVS and learn all about how it works. sign up for a free AWS account Creating an Amazon IVS Channel I've blogged about how to , but the quickest way to create a one-off channel is via the AWS CLI ( ). The command to create a channel is: get started creating an Amazon IVS channel install docs $ aws ivs create-channel --name lofi-demo --latency-mode LOW --type BASIC This will return some important information about your stream: {
  "channel": {
    "arn": "arn:aws:ivs:us-east-1:[redacted]:channel/[redacted]",
    "authorized": false,
    "ingestEndpoint": "f99084460c35.global-contribute.live-video.net",
    "latencyMode": "LOW",
    "name": "lofi-demo",
    "playbackUrl": "https://f99084460c35.us-east-1.playback.live-video.net/api/video/v1/us-east-1.[redacted].channel.[redacted].m3u8",
    "recordingConfigurationArn": "",
    "tags": {},
    "type": "BASIC"
  },
  "streamKey": {
    "arn": "arn:aws:ivs:us-east-1:[redacted]:stream-key/[redacted]",
    "channelArn": "arn:aws:ivs:us-east-1:[redacted]:channel/[redacted]",
    "tags": {},
    "value": "sk_us-east-1[redacted]"
  }
} Keep this info handy, as we'll need the , , and in just a bit. ingestEndpoint playbackUrl streamKey Broadcasting to the Lofi Channel with the Web Broadcast SDK Now that we have a channel created, we can immediately start broadcasting to it. If you've done any live streaming, you're probably familiar with desktop streaming software (like OBS, Streamlabs Desktop, etc). Instead of using third-party software, we're going to write some code to broadcast our lofi stream directly from a browser. : You should have your own animation handy and an MP3 track. - please only utilize assets that you have obtained the proper licensing to use. If your animation is a GIF, you'll want to convert it to an MP4 file (there are plenty of tools available online for this). Before We Code Copyright is a thing The first step is to create an HTML page that includes the Web Broadcast SDK script, and contains a element for the broadcast preview, a element for the source animation, and a button to start the broadcast. The of the tag should point to your animation source and should have a width and height of (we're going to preview the video on the in just a bit, so no need for the source video to be visible). <canvas> <video> src <video> 1px <canvas> <!doctype html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta http-equiv="X-UA-Compatible" content="IE=edge">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Lofi Radio</title>
  <script src="https://web-broadcast.live-video.net/1.2.0/amazon-ivs-web-broadcast.js"></script>
</head>
<body>

  <canvas id="broadcast-preview"></canvas>

  <video id="src-video" 
    src="/video/lofi.mp4" 
    style="width: 1px; height: 1px;"
    muted loop></video>

  <button id="broadcast-btn">Broadcast</button>

</body>
</html> Next, create an external JS file called (don't forget to include it in your HTML above). Start off this file with a listener and define an function to get things set up. In the function, we'll create an instance of the from the Web Broadcast SDK. We'll need to pass it the from our channel. We'll also create a click handler for the broadcast button that will call a function that we'll define in just a bit. lofi.js DOMContentLoaded init() init() IVSBroadcastClient ingestEndpoint toggleBroadcast() const init = () => {
  window.broadcastClient = IVSBroadcastClient.create({
    streamConfig: IVSBroadcastClient.BASIC_FULL_HD_LANDSCAPE,
    ingestEndpoint: [Your ingestEndpoint],
  });
  document.getElementById('broadcast-btn').addEventListener('click', toggleBroadcast);
};
document.addEventListener('DOMContentLoaded', init); To keep things simple, we're setting the into the window scope. In reality, you'll probably be using a framework so you'll usually avoid using the global scope like this. Note: broadcastClient window Next, we need to create our video stream and attach it to the . Modify the function as follows: broadcastClient init() const init = () => {
  window.broadcastClient = IVSBroadcastClient.create({
    streamConfig: IVSBroadcastClient.BASIC_FULL_HD_LANDSCAPE,
    ingestEndpoint: [Your ingestEndpoint],
  });

  window.video = document.getElementById('src-video');
  window.broadcastClient.addImageSource(window.video, 'video-track', { index: 0 });
  const preview = document.getElementById('broadcast-preview');
  window.broadcastClient.attachPreview(preview);

  document.getElementById('broadcast-btn').addEventListener('click', toggleBroadcast);
}; At this point, we've got our animation source created and added to the client. Next, let's add a function that will attach the MP3 audio source to the stream. createAudioStream() const createAudioStream = async () => {
  /* Music from Uppbeat (free for Creators!): https://uppbeat.io/t/vens-adams/alone-in-kyoto */
  const audioContext = new AudioContext();
  const mp3 = await fetch('/audio/alone-in-kyoto.mp3');
  const mp3Buffer = await mp3.arrayBuffer();
  const audioBuffer = await audioContext.decodeAudioData(mp3Buffer);
  const streamDestination = audioContext.createMediaStreamDestination();
  const bufferSource = audioContext.createBufferSource();
  bufferSource.buffer = audioBuffer;
  bufferSource.start(0);
  bufferSource.connect(streamDestination);
  bufferSource.loop = true; 
  window.broadcastClient.addAudioInputDevice(streamDestination.stream, 'audio-track');
}; And modify the function to add and call . init() async await createAudioStream() const init = async () => {
  window.broadcastClient = IVSBroadcastClient.create({
    streamConfig: IVSBroadcastClient.BASIC_FULL_HD_LANDSCAPE,
    ingestEndpoint: [Your ingestEndpoint],
  });

  window.video = document.getElementById('src-video');
  window.broadcastClient.addImageSource(window.video, 'video-track', { index: 0 });
  const preview = document.getElementById('broadcast-preview');
  window.broadcastClient.attachPreview(preview);

  await createAudioStream();

  document.getElementById('broadcast-btn').addEventListener('click', toggleBroadcast);
}; Finally, we can define the function to start/stop the stream when the button is clicked. toggleBroadcast() : you'll need to plug in your at this point. Treat this like any other sensitive credential and protect it from being committed to source control or included in a file that can be accessed by others. Note streamKey const toggleBroadcast = () => {
  if (!window.isBroadcasting) {
    window.video.play();
    window.broadcastClient
      .startBroadcast([your streamKey])
      .then(() => {
        window.isBroadcasting = true;
      })
      .catch((error) => {
        window.isBroadcasting = false;
        console.error(error);
      });
  }
  else {
    window.broadcastClient.stopBroadcast();
    window.video.pause();
    window.isBroadcasting = false;
  }
}; Now we can save and run the application. Here's how my page looks (with a bit of CSS applied for styling and layout). When I click the button, the animation begins to play and our live stream is broadcast to our channel. Broadcast : You will not be able to hear the channel audio from the broadcast tool. We'll look at playback in just a bit. Note Playing Back the Lofi Channel I've blogged about with Amazon IVS before, but we'll create a quick and simple player here to complete the demo. Create an HTML file, include the Amazon IVS player SDK, add a element, create an instance of the player (plug in your ), and initialize playback. live stream playback <video> playbackUrl <!doctype html>
<html lang="en">

<head>
  <meta charset="UTF-8">
  <meta http-equiv="X-UA-Compatible" content="IE=edge">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Lofi Radio Playback</title>
  <script src="https://player.live-video.net/1.14.0/amazon-ivs-player.min.js"></script>
  <script>
    document.addEventListener('DOMContentLoaded', () => {
      const ivsPlayer = IVSPlayer.create();
      ivsPlayer.attachHTMLVideoElement(document.getElementById('video-player'));
      ivsPlayer.load([your playbackUrl]);
      ivsPlayer.play();
    })
  </script>
</head>

<body>
  <video id="video-player" controls autoplay playsinline></video>
</body>

</html> And now we can playback our lofi radio stream in it's own page! The nice thing about our code is that both the audio and animation will continue on a loop until your channel stops broadcasting. Bonus: Broadcasting Without a Browser Another option for broadcasting your own lofi stream is to avoid having any user interface and broadcast in headless mode with FFMPEG (more about that ). Here's an example command that would work for this channel. here $ ffmpeg \
    -re \
    -stream_loop -1 \
    -i ./public/video/lofi.mp4 \
    -stream_loop -1 \
    -i ./public/audio/alone-in-kyoto.mp3 \
    -map 0:v:0 \
    -map 1:a:0 \
    -c:v libx264 \
    -b:v 6000K \
    -maxrate 6000K \
    -pix_fmt yuv420p \
    -s 1920x1080 \
    -profile:v main \
    -preset veryfast \
    -force_key_frames "expr:gte(t,n_forced*2)" \
    -x264opts "nal-hrd=cbr:no-scenecut" \
    -acodec aac \
    -ab 160k \
    -ar 44100 \
    -f flv \
    $DEMO_STREAM_INGEST_ENDPOINT/$DEMO_STREAM_KEY Summary In this post, we learned how to create our own lofi radio live stream with Amazon IVS. To make your own lofi stream even more interactive, add with both and chat moderation. If you have any questions about this demo, please leave a comment below. live chat automated manual Also published . here

How to Build Your Own "LoFi Radio" Station and Stream it Live With Amazon IVS

In a previous post of this series [1], we introduced how to use the Python-based graph analysis library NetworkX [2] and [3] to analyze the character relationships in “Game of Thrones”. Nebula Graph In this post, we are going to explore how to use the Java-based graph analysis library JGraphT [4] and the diagramming library mxGraph [5] to visualize the changes of correlation between the S&P 100 Index stocks [6] over time. Dataset Processing The main analysis method used in this post refers to [7] and [8]. There are two datasets: the vertex set and the edge set. Stock Data (Vertex Set) Select the following S&P 100 Index stocks, and model each stock as a vertex. The properties of each vertex are the stock code and the industry of the listed company. Table 1: The vertex set sample Stock Relationship (Edge/Relationship Set) Each edge has only one property, i.e. the weight. The weight of an edge indicates the daily return rate similarity of the listed companies represented by the vertices (stocks) on both ends of the edge. The similarity algorithm refers to [7] and [8]: analyze the time-series correlation of the individual stocks’ daily return rate over a period of time (from 2014-01-01 to 2020-01-01), and define the distance between the individual stocks (i.e. the edge weight) as follows. Through such processing, we get the range of the distance as [0,2]. That means the more distant the two stocks, the lower the correlation between their return rates. Table 2: The edge set sample Such a vertex set and an edge set form a graph network, which can be stored in the Nebula Graph database. JGraphT JGraphT is an open-source Java class library that provides not only a variety of efficient and common graph data structures but also many useful algorithms for solving the most common graph problems. The features of JGraphT are as follows: Supports directed edges, undirected edges, weighted edges, non-weighted edges, etc. Supports simple graphs, multigraphs, and pseudographs. Provides dedicated iterators (DFS, BFS, etc.) for graph traversals. Provides a large number of commonly used graph algorithms, such as path lookup, isomorphism detection, coloring, common ancestors, wandering, connectivity, matching, cycle detection, partitioning, cutting, flow, and centrality. Provides easy import from/export to GraphViz. The exported GraphViz file can be applied by the visualization tool Gephi for analysis and demonstration. Supports convenient graph network generating while used with tools such as JGraphX，mxGraph, and Guava Graphs Generators. Next, let’s try it out. 1. Create a directed graph in JGraphT. org.jgrapht.*; org.jgrapht.graph.*; org.jgrapht.nio.*; org.jgrapht.nio.dot.*; org.jgrapht.traverse.*; java.io.*; java.net.*; java.util.*;

Graph<URI, DefaultEdge> g = DefaultDirectedGraph<>(DefaultEdge.class);

URI google = URI( );
URI wikipedia = URI( );
URI jgrapht = URI( ); import import import import import import import import new new "http://www.google.com" new "http://www.wikipedia.org" new "http://www.jgrapht.org" 2. Add vertices. g.add ;
g.add ;
g.add ; Vertex( ) google Vertex( ) wikipedia Vertex( ) jgrapht 3. Add edges. g.add ;
g.add ;
g.add ;
g.add ; // Add edges to create a linked structure Edge( , ) jgrapht wikipedia Edge( , ) google jgrapht Edge( , ) google wikipedia Edge( , ) wikipedia google The Nebula Graph database JGraphT usually uses local files as data sources. That’s fine when you’re doing static network research, but if the graph network is constantly changing, like a graph network of the stock data, which changes every day, it’s a little bit of a hassle to generate a new static file, then load it and then analyze it every time. Ideally, the entire change process can be written to a database persistently, and subgraphs or the complete graph can be loaded directly from the database in real-time for analysis. In this post, we use Nebula Graph as the graph database for storing the graph data. Nebula Graph’s Java client nebula-java [11] provides two ways of accessing Nebula Graph. One is interacting with the query engine layer [13] through the graph query language nGQL [12], which is usually suitable for accessing subgraphs and supports complex semantics; the other is to directly interact with the underlying storage layer (the storaged process) [14] through the APIs, which is used to obtain the complete set of vertices and edges. In addition to accessing Nebula Graph itself, nebula-java provides examples of interacting with Neo4j [15], JanusGraph [16], Spark [17], and others. In this post, we use the APIs to access the storage layer (the storaged process) directly to get all the vertices and edges. The following two interfaces can be used to read all the vertex and edge data. ; ; // space is the name of the graph space to be scanned, and returnCols is the vertex/edge and its properties to be read. // Format of returnCols parameter: {tag1Name: prop1, prop2, tag2Name: prop3, prop4, prop5} Iterator<ScanVertexResponse> scanVertex (
            Stringspace, Map<String, List<String>>returnCols) Iterator<ScanEdgeResponse> scanEdge (
            Stringspace, Map<String, List<String>>returnCols) 1. Initialize a client and a ScanVertexProcessor. ScanVertexProcessor is used to decode the read vertex data: MetaClientImpl metaClientImpl = MetaClientImpl(metaHost, metaPort);
metaClientImpl.connect();
StorageClient storageClient = StorageClientImpl(metaClientImpl);
Processor processor = ScanVertexProcessor(metaClientImpl); new new new 2. Call the scanVertex interface, which returns an iterator for the scanVertexResponse object: Iterator<ScanVertexResponse> iterator =
                storageClient.scanVertex(spaceName, returnCols); 3. Keep reading the data in the scanVertexResponse object that the iterator points to until all of the data is read. The read vertex data is saved and later added to the graph structure of JGraphT. (iterator.hasNext()) {
  ScanVertexResponse response = iterator.next(); (response == ) {
    log.error( ); ;
  }
  
  Result result =  processor.process(spaceName, response);
  results.addAll(result.getRows(TAGNAME));
} while if null "Error occurs while scan vertex" break Reading edge data is similar to the above process. Analyze the Graph in JGraphT 1. Create an undirected and weighted graph in JGraphT : Graph<String, MyEdge> graph = GraphTypeBuilder
                .undirected()
    .weighted( )
    .allowingMultipleEdges( )
    .allowingSelfLoops( )
    .vertexSupplier(SupplierUtil.createStringSupplier())
    .edgeSupplier(SupplierUtil.createSupplier(MyEdge.class))
    .buildGraph(); true true false 2. Add the vertex and edge data read in the last step from the Nebula Graph space to the graph: (VertexDomain vertex : vertexDomainList){
    graph.addVertex(vertex.getVid().toString());
    stockIdToName.put(vertex.getVid().toString(), vertex);
} (EdgeDomain edgeDomain : edgeDomainList){
    graph.addEdge(edgeDomain.getSrcid().toString(), edgeDomain.getDstid().toString());
    MyEdge newEdge = graph.getEdge(edgeDomain.getSrcid().toString(), edgeDomain.getDstid().toString());
    graph.setEdgeWeight(newEdge, edgeDomain.getWeight());
} for for 3. Like the analysis algorithms mentioned in [7] and [8], use Prim’s minimum-spanning tree algorithm for the preceding graph and call the encapsulated drawGraph interface to draw the graph. Prim’s algorithm is an algorithm in graph theory that searches for a minimum spanning tree in a weighted connected graph. In other words, the tree formed by the edge subset searched by this algorithm not only includes all vertices in the connected graph but also has the minimum sum of weights of all edges. SpanningTreeAlgorithm.SpanningTree pMST = PrimMinimumSpanningTree(graph).getSpanningTree();

Legend.drawGraph(pMST.getEdges(), filename, stockIdToName); new 4. The drawGraph method encapsulates parameter settings such as the layout of the drawing. This method renders stocks in the same sector in the same color, grouping close stocks together. {
  
... { mxGraph graph = mxGraph();
     Object parent = graph.getDefaultParent(); graph.getModel().beginUpdate();
     mxStylesheet myStylesheet =  graph.getStylesheet();
     graph.setStylesheet(setMsStylesheet(myStylesheet));

     Map<String, Object> idMap = HashMap<>();
     Map<String, String> industryColor = HashMap<>(); colorIndex = ; (MyEdge edge : edges) {
       Object src, dst; (!idMap.containsKey(edge.getSrc())) {
         VertexDomain srcNode = idVertexMap.get(edge.getSrc());
         String nodeColor; (industryColor.containsKey(srcNode.getIndustry())){
           nodeColor = industryColor.get(srcNode.getIndustry());
         } {
           nodeColor = COLOR_LIST[colorIndex++];
           industryColor.put(srcNode.getIndustry(), nodeColor);
         }
         src = graph.insertVertex(parent, , srcNode.getName(), , , , , + nodeColor);
         idMap.put(edge.getSrc(), src);
       } {
         src = idMap.get(edge.getSrc());
       } (!idMap.containsKey(edge.getDst())) {
         VertexDomain dstNode = idVertexMap.get(edge.getDst());

         String nodeColor; (industryColor.containsKey(dstNode.getIndustry())){
           nodeColor = industryColor.get(dstNode.getIndustry());
         } {
           nodeColor = COLOR_LIST[colorIndex++];
           industryColor.put(dstNode.getIndustry(), nodeColor);
         }

         dst = graph.insertVertex(parent, , dstNode.getName(), , , , , + nodeColor);
         idMap.put(edge.getDst(), dst);
       } {
         dst = idMap.get(edge.getDst());
       }
       graph.insertEdge(parent, , , src, dst);
     }


     log.info( + idMap.size());
     log.info( + industryColor.size());

     mxFastOrganicLayout layout = mxFastOrganicLayout(graph);
     layout.setMaxIterations( ); layout.execute(parent);

     graph.getModel().endUpdate(); BufferedImage image = createBufferedImage(graph, , , Color.WHITE, , ); File file = File(filename);
     ImageIO.write(image, , file);

   }
  
  ...

} public class Legend IOException public static void drawGraph (Set<MyEdge> edges, String filename, Map<String, VertexDomain> idVertexMap) throws // Creates graph with model new // set style new new int 0 for if if else null 0 0 105 50 "fillColor=" else if if else null 0 0 105 50 "fillColor=" else null "" "vertice " "colorsize " new 2000 //layout.setMinDistanceLimit(10D); // Creates an image than can be saved using ImageIO null 1 true null // For the sake of this example we display the image in a window // Save as JPEG new "JPEG" 5. Visualize the data. The color of each vertex in Figure 1 represents its industry. We can see that the stocks with high business similarity have been clustered together, but some stocks with no obvious correlation have also been clustered together, the reason for which needs to be studied separately. Figure 1: Clustering based on the stock data from 2014-01-01 to 2020-01-01 6. Some other dynamic exploration based on different time windows. The preceding conclusion is mainly based on the stock aggregation from 2014-01-01 to 2020-01-01. We also made other attempts: Use a sliding window of 2 years and the same analysis method to observe if the clustered groups would change over time. Figure 2: Clustering based on the stock data from 2014-01-01 to 2016-01-01 Figure 3: Clustering based on the stock data from 2015-01-01 to 2017-01-01 Figure 4: Clustering based on the stock data from 2016-01-01 to 2018-01-01 Figure 5: Clustering based on the stock data from 2017-01-01 to 2019-01-01 Figure 6: Clustering based on the stock data from 2018-01-01 to 2020-01-01 According to a rough analysis, with the change of time window, the clustering of stocks within most industries maintains relatively good, which means that with the change of time, all kinds of stocks within the same industry maintain relatively high correlation. Disclaimer This post should not be taken as investment advice. Due to the situation of trade suspension, circuit breakers, trading limits, transfers, mergers and acquisitions, changes of the main business, etc., the data processing in this post may be incorrect. We have not checked all the data piece by piece. Limited by time, this post only selects the data of 100 stock samples in the past six years, and only adopts the method of minimum expansion tree to do the clustering and classification. In the future, maybe we can use larger data sets (such as U.S. stocks, derivatives, and digital currencies) to try more methods in machine learning. For the code used in this post, please see [18]. References [1] Analyzing Relationships in Game of Thrones With NetworkX, Gephi, and Nebula Graph (Part One) https://nebula-graph.io/posts/game-of-thrones-relationship-networkx-gephi-nebula-graph/ [2] Analyzing Relationships in Game of Thrones With NetworkX, Gephi, and Nebula Graph (Part Two) https://nebula-graph.io/posts/game-of-thrones-relationship-networkx-gephi-nebula-graph-part-two/ [3] NetworkX: a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. https://networkx.github.io/ [4] Nebula Graph: A powerfully distributed, scalable, lightning-fast graph database written in C++. https://nebula-graph.io/ [5] JGraphT: a Java library of graph theory data structures and algorithms. https://jgrapht.org/ [6] mxGraph: JavaScript diagramming library that enables interactive graph and charting applications. https://jgraph.github.io/mxgraph/ [7] Bonanno, Giovanni & Lillo, Fabrizio & Mantegna, Rosario. (2000). High-frequency Cross-correlation in a Set of Stocks. arXiv.org, Quantitative Finance Papers. 1. 10.1080/713665554. [8] Mantegna, R.N. Hierarchical structure in financial markets. Eur. Phys. J. B 11, 193–197 (1999). [9] https://graphviz.org/ [10] https://gephi.org/ [11] https://github.com/vesoft-inc/nebula-java [12] Nebula Graph Query Language (nGQL). https://docs.nebula-graph.io/manual-EN/1.overview/1.concepts/2.nGQL-overview/ [13] Nebula Graph Query Engine. https://github.com/vesoft-inc/nebula-graph [14] Nebula-storage: A distributed consistent graph storage. https://github.com/vesoft-inc/nebula-storage [15] Neo4j. www.neo4j.com [16] JanusGraph. janusgraph.org [17] Apache Spark. . spark.apache.org [18] https://github.com/Judy1992/nebula_scan Previously published at https://nebula-graph.io/posts/stock-interrelation-analysis-nebula-graph-machine-learning/

How to Explore the S&P 100 Index Stocks Using Graph Machine Learning

The Web Audio API provides a powerful and versatile system for controlling audio on the Web, allowing developers to choose audio sources, add effects to audio, create audio visualizations, apply spatial effects (such as panning) and much more. Web audio concepts and usage The Web Audio API involves handling audio operations inside an , and has been designed to allow . Basic audio operations are performed with , which are linked together to form an . Several sources — with different types of channel layout — are supported even within a single context. This modular design provides the flexibility to create complex audio functions with dynamic effects. audio context modular routing audio nodes audio routing graph Audio nodes are linked into chains and simple webs by their inputs and outputs. They typically start with one or more sources. Sources provide arrays of sound intensities (samples) at very small timeslices, often tens of thousands of them per second. These could be either computed mathematically (such as ), or they can be recordings from sound/video files (like and ) and audio streams ( ). OscillatorNode AudioBufferSourceNode MediaElementAudioSourceNode MediaStreamAudioSourceNode In fact, sound files are just recordings of sound intensities themselves, which come in from microphones or electric instruments, and get mixed down into a single, complicated wave. Outputs of these nodes could be linked to inputs of others, which mix or modify these streams of sound samples into different streams. A common modification is multiplying the samples by a value to make them louder or quieter (as is the case with ). GainNode Once the sound has been sufficiently processed for the intended effect, it can be linked to the input of a destination ( ), which sends the sound to the speakers or headphones. This last connection is only necessary if the user is supposed to hear the audio. AudioContext.destination A simple, typical workflow for web audio would look something like this: Create audio context Inside the context, create sources — such as , oscillator, stream <audio> Create effects nodes, such as reverb, biquad filter, panner, compressor Choose final destination of audio, for example your system speakers Connect the sources up to the effects, and the effects to the destination. Timing is controlled with high precision and low latency, allowing developers to write code that responds accurately to events and is able to target specific samples, even at a high sample rate. So applications such as drum machines and sequencers are well within reach. The Web Audio API also allows us to control how audio is . Using a system based on a , it allows control of the and deals with or induced by a moving source (or moving listener). spatialized source-listener model panning model distance-induced attenuation doppler shift You can read about the theory of the Web Audio API in a lot more detail in our article . Basic concepts behind Web Audio API Web Audio API target audience The Web Audio API can seem intimidating to those that aren't familiar with audio or music terms, and as it incorporates a great deal of functionality it can prove difficult to get started if you are a developer. It can be used to simply incorporate audio into your website or application, by , or . However, it can also be used to create interactive instruments. With that in mind, it is suitable for both developers and musicians alike. providing atmosphere like futurelibrary.no auditory feedback on forms advanced We have a for those that are familiar with programming but need a good introduction to some of the terms and structure of the API. simple introductory tutorial There's also a article, to help you understand the way digital audio works, specifically in the realm of the API. This also includes a good introduction to some of the concepts the API is built upon. Basic Concepts Behind Web Audio API Learning coding is like playing cards — you learn the rules, then you play, then you go back and learn the rules again, then you play again. So if some of the theory doesn't quite fit after the first tutorial and article, there's an which extends the first one to help you practise what you've learnt, and apply some more advanced techniques to build up a step sequencer. advanced tutorial We also have other tutorials and comprehensive reference material available that covers all features of the API. See the sidebar on this page for more. If you are more familiar with the musical side of things, are familiar with music theory concepts, want to start building instruments, then you can go ahead and start building things with the advance tutorial and others as a guide (the above linked tutorial covers scheduling notes, creating bespoke oscillators and envelopes, as well as an LFO among other things.) If you aren't familiar with the programming basics, you might want to consult some beginner's JavaScript tutorials first and then come back here — see our for a great place to begin. Beginner's JavaScript learning module Web Audio API Interfaces The Web Audio API has a number of interfaces and associated events, which we have split up into nine categories of functionality. General audio graph definition General containers and definitions that shape audio graphs in Web Audio API usage. AudioContext The interface represents an audio-processing graph built from audio modules linked together, each represented by an . An audio context controls the creation of the nodes it contains and the execution of the audio processing, or decoding. You need to create an before you do anything else, as everything happens inside a context. AudioContext AudioNode AudioContext AudioContextOptions The dictionary is used to provide options when instantiating a new . AudioContextOptions AudioContext AudioNode The interface represents an audio-processing module like an (e.g. an HTML or element), , (e.g. a filter like , or like ). AudioNode audio source <audio> <video> audio destination intermediate processing module BiquadFilterNode volume control GainNode AudioParam The interface represents an audio-related parameter, like one of an . It can be set to a specific value or a change in value, and can be scheduled to happen at a specific time and following a specific pattern. AudioParam AudioNode AudioParamMap Provides a maplike interface to a group of interfaces, which means it provides the methods , , , , and , as well as a property. AudioParam forEach() get() has() keys() values() size BaseAudioContext The interface acts as a base definition for online and offline audio-processing graphs, as represented by and respectively. You wouldn't use directly — you'd use its features via one of these two inheriting interfaces. BaseAudioContext AudioContext OfflineAudioContext BaseAudioContext The ended event The event is fired when playback has stopped because the end of the media was reached. ended Interfaces that define audio sources for use in the Web Audio API. Defining audio sources AudioScheduledSourceNode The is a parent interface for several types of audio source node interfaces. It is an . AudioScheduledSourceNode AudioNode OscillatorNode The interface represents a periodic waveform, such as a sine or triangle wave. It is an audio-processing module that causes a given of wave to be created. OscillatorNode AudioNode frequency AudioBuffer The interface represents a short audio asset residing in memory, created from an audio file using the method, or created with raw data using . Once decoded into this form, the audio can then be put into an . AudioBuffer AudioContext.decodeAudioData() AudioContext.createBuffer() AudioBufferSourceNode AudioBufferSourceNode The interface represents an audio source consisting of in-memory audio data, stored in an . It is an that acts as an audio source. AudioBufferSourceNode AudioBuffer AudioNode MediaElementAudioSourceNode The interface represents an audio source consisting of an HTML5 or element. It is an that acts as an audio source. MediaElementAudioSourceNode <audio> <video> AudioNode MediaStreamAudioSourceNode The interface represents an audio source consisting of a (such as a webcam, microphone, or a stream being sent from a remote computer). If multiple audio tracks are present on the stream, the track whose comes first lexicographically (alphabetically) is used. It is an that acts as an audio source. MediaStreamAudioSourceNode MediaStream id AudioNode MediaStreamTrackAudioSourceNode A node of type represents an audio source whose data comes from a . When creating the node using the method to create the node, you specify which track to use. This provides more control than . MediaStreamTrackAudioSourceNode MediaStreamTrack createMediaStreamTrackSource() MediaStreamAudioSourceNode Defining audio effects filters Interfaces for defining effects that you want to apply to your audio sources. BiquadFilterNode The interface represents a simple low-order filter. It is an that can represent different kinds of filters, tone control devices, or graphic equalizers. A always has exactly one input and one output. BiquadFilterNode AudioNode BiquadFilterNode ConvolverNode The interface is an that performs a Linear Convolution on a given , and is often used to achieve a reverb effect. ConvolverNode AudioNode AudioBuffer DelayNode The interface represents a ; an audio-processing module that causes a delay between the arrival of an input data and its propagation to the output. DelayNode delay-line AudioNode DynamicsCompressorNode The interface provides a compression effect, which lowers the volume of the loudest parts of the signal in order to help prevent clipping and distortion that can occur when multiple sounds are played and multiplexed together at once. DynamicsCompressorNode GainNode The interface represents a change in volume. It is an audio-processing module that causes a given to be applied to the input data before its propagation to the output. GainNode AudioNode gain WaveShaperNode The interface represents a non-linear distorter. It is an that use a curve to apply a waveshaping distortion to the signal. Beside obvious distortion effects, it is often used to add a warm feeling to the signal. WaveShaperNode AudioNode PeriodicWave Describes a periodic waveform that can be used to shape the output of an . OscillatorNode IIRFilterNode Implements a general (IIR)  filter; this type of filter can be used to implement tone control devices and graphic equalizers as well. infinite impulse response Once you are done processing your audio, these interfaces define where to output it. Defining audio destinations AudioDestinationNode The interface represents the end destination of an audio source in a given context — usually the speakers of your device. AudioDestinationNode MediaStreamAudioDestinationNode The interface represents an audio destination consisting of a with a single AudioMediaStreamTrack, which can be used in a similar way to a obtained from . It is an that acts as an audio destination. MediaStreamAudioDestinationNode WebRTC MediaStream MediaStream getUserMedia() AudioNode If you want to extract time, frequency, and other data from your audio, the AnalyserNode is what you need. Data analysis and visualization AnalyserNode The interface represents a node able to provide real-time frequency and time-domain analysis information, for the purposes of data analysis and visualization. AnalyserNode To split and merge audio channels, you'll use these interfaces. Splitting and merging audio channels ChannelSplitterNode The interface separates the different channels of an audio source out into a set of outputs. ChannelSplitterNode mono ChannelMergerNode The interface reunites different mono inputs into a single output. Each input will be used to fill a channel of the output. ChannelMergerNode Audio spatialization These interfaces allow you to add audio spatialization panning effects to your audio sources. AudioListener The interface represents the position and orientation of the unique person listening to the audio scene used in audio spatialization. AudioListener PannerNode The interface represents the position and behavior of an audio source signal in 3D space, allowing you to create complex panning effects. PannerNode StereoPannerNode The interface represents a simple stereo panner node that can be used to pan an audio stream left or right. StereoPannerNode Using audio worklets, you can define custom audio nodes written in JavaScript or . Audio worklets implement the interface, a lightweight version of the interface. Audio worklets are enabled by default for Chrome 66 or later. Audio processing in JavaScript WebAssembly Worklet Worker AudioWorklet The AudioWorklet interface is available via , and allows you to add new modules to the audio worklet. BaseAudioContext.audioWorklet AudioWorkletNode The interface represents an that is embedded into an audio graph and can pass messages to the corresponding . AudioWorkletNode AudioNode AudioWorkletProcessor AudioWorkletProcessor The interface represents audio processing code running in a that generates, processes, or analyses audio directly, and can pass messages to the corresponding . AudioWorkletProcessor AudioWorkletGlobalScope AudioWorkletNode AudioWorkletGlobalScope The interface is a -derived object representing a worker context in which an audio processing script is run; it is designed to enable the generation, processing, and analysis of audio data directly using JavaScript in a worklet thread. Before audio worklets were defined, the Web Audio API used the for JavaScript-based audio processing. Because the code runs in the main thread, they have bad performance. The is kept for historic reasons but is marked as deprecated and will be removed in a future version of the specification. AudioWorkletGlobalScope WorkletGlobalScope Obsolete: script processor nodes ScriptProcessorNode ScriptProcessorNode ScriptProcessorNode The interface allows the generation, processing, or analyzing of audio using JavaScript. It is an audio-processing module that is linked to two buffers, one containing  the current input, one containing the output. An event, implementing the interface, is sent to the object each time the input buffer contains new data, and the event handler terminates when it has filled the output buffer with data. ScriptProcessorNode AudioNode AudioProcessingEvent audioprocess (event) The event is fired when an input buffer of a Web Audio API is ready to be processed. audioprocess ScriptProcessorNode AudioProcessingEvent The represents events that occur when a input buffer is ready to be processed. Web Audio API AudioProcessingEvent ScriptProcessorNode It is possible to process/render an audio graph very quickly in the background — rendering it to an rather than to the device's speakers — with the following. Offline/background audio processing AudioBuffer OfflineAudioContext The interface is an interface representing an audio-processing graph built from linked together s. In contrast with a standard AudioContext, an OfflineAudioContext doesn't really render the audio but rather generates it, , in a buffer. OfflineAudioContext AudioContext AudioNode as fast as it can complete (event) The event is fired when the rendering of an is terminated. complete OfflineAudioContext OfflineAudioCompletionEvent The represents events that occur when the processing of an is terminated. The event implements this interface. OfflineAudioCompletionEvent OfflineAudioContext complete Obsolete interfaces The following interfaces were defined in old versions of the Web Audio API spec, but are now obsolete and have been replaced by other interfaces. JavaScriptNode Used for direct audio processing via JavaScript. This interface is obsolete, and has been replaced by . ScriptProcessorNode WaveTableNode Used to define a periodic waveform. This interface is obsolete, and has been replaced by . PeriodicWave Examples You can find a number of examples at our on GitHub. webaudio-example repo Specifications Browser compatibility AudioContext See also Tutorials/guides Basic concepts behind Web Audio API Using the Web Audio API Advanced techniques: creating sound, sequencing, timing, scheduling Autoplay guide for media and Web Audio APIs Using IIR filters Visualizations with Web Audio API Web audio spatialisation basics Controlling multiple parameters with ConstantSourceNode Mixing Positional Audio and WebGL Developing Game Audio with the Web Audio API Porting webkitAudioContext code to standards based AudioContext Libraries : a simple library for playing specific tones/notes using the Web Audio API. Tones : a framework for creating interactive music in the browser. Tone.js : a JS audio library that defaults to and falls back to , as well as providing other useful features. howler.js Web Audio API HTML5 Audio : jQuery-style chaining of AudioNodes, mixer-style sends/returns, and more. Mooog : Web Audio API Library for Synthesizer, Effects, Visualization, Recording ... etc XSound : HTML5 video language lab web application using the Web Audio API to record and combine video and audio from different sources into a single file ( ) OpenLang source on GitHub : Simplifies web audio visualization ( ) Pts.js guide Related topics Web media technologies Guide to media types and formats on the web See also Using the Web Animations API Web Animations demos Polyfill Firefox's current implementation: AreWeAnimatedYet Browser support test Credits Source: https://developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API Published under licence Open CC Attribution ShareAlike 3.0

Getting Started with Web Audio API

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